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Report of the Expert Committee to Review the Extant Economic Capital Framework of the Reserve Bank of India

CONTENTS
Introduction
Executive Summary
1. An Overview of the Role and Relevance of Central Banks’ Financial Resilience
I. Central bank capital and its role in monetary and financial stability
II. Survey of literature
III. Central banks’ unique risk environment and their risk management frameworks
2. Review of Central Banking Practices
I. Various approaches towards strengthening the central banks’ financial resilience
II. Central banks’ economic capital levels as defined under the ECF
3. The RBI’s Public Policy Mandate, the Impact on its Balance Sheet and its Risks
I. The RBI’s functions
II. Impact of RBI’s functions and public policy objectives on its balance sheet
III. The RBI’s risks
4. Review of the Economic Capital Framework and Staggered Surplus Distribution Policy of RBI
I. A historical perspective of risk provisioning in the RBI
II. The extant Economic Capital Framework
III. The Staggered Surplus Distribution Policy
IV. Developments subsequent to the introduction of the SSDP
V. Certain concerns with regard to the extant ECF
VI. RBI’s rationale for ECF parameterization
VII. ECF-SSDP and risk provisioning
VIII. Quality of RBI’s economic capital
IX. The ECF going forward
X. The opportunity cost of RBI’s capital
XI. The Surplus Distribution Policy going forward
XII. Determining whether available risk provisions are in excess of required risk provisions
XIII. Treatment of excess unrealized revaluation balances
XIV. Treatment of excess realized risk provisions
XV. Interim dividend and aligning RBI’s financial year with the Government’s fiscal year
XVI. Periodicity of review of the ECF
5. Summary of Recommendations
References
Annex I: Memorandum for constitution of the Expert Committee to Review the Extant ECF of RBI
Annex II: Nomination of Shri Rajiv Kumar (Finance Secretary) to Expert Committee
Annex III: Economic Capital Framework of other central banks
Annex IV: Central banks with risk transfer mechanisms
Annex V: Surplus distribution by an advanced economy central bank
Annex VI: Rating methodologies/ relevant ratings of Standard & Poor’s (S&P), Moody’s and DBRS
Annex VII: Previously adopted methodologies for assessment of risk provisioning requirements of the RBI
Annex VIII: Constraints on monetization of revaluation balances by the RBI
Annex IX: An outline of the methodologies used in the ECF
Annex X: Risk Tolerance Statement (Risk Philosophy) of the Reserve Bank of India
Annex XI: Expansion of eligible assets classes by select central banks following the GFC
Annex XII: Recapitalization of commercial banks by national treasuries
Annex XIII: Projection of RBI’s balance sheet and net income till 2022-23
Select Abbreviations and Definitions

Letter

A Introduction

The Reserve Bank of India (RBI) has developed an Economic Capital Framework (ECF) to provide an objective, rule-based, transparent methodology for determining the appropriate level of risk provisions to be made under Section 47 of the Reserve Bank of India Act, 1934. The framework was developed in 2014–15, and while it was used to inform the risk provisioning and surplus distribution decisions for that year, it was formally operationalized in 2015–16. The ECF was supplemented by a Staggered Surplus Distribution Policy (SSDP) in 2016-17 to smoothen the cyclicality in RBI’s economic capital and incorporate a certain degree of flexibility in surplus distribution.

2. As decided by the Central Board of the RBI in its meeting held on November 19, 2018, the RBI, in consultation with the Government of India (Government), constituted an Expert Committee to review the extant ECF of the RBI. Shri Subhash Chandra Garg, the then Secretary, Department of Economic Affairs, was initially a member of the Committee. Subsequently, with the appointment of Shri Rajiv Kumar, Finance Secretary, the composition of the Committee is as under:

(i) Dr. Bimal Jalan

Chairman

(ii) Dr. Rakesh Mohan

Vice-Chairman

(iii) Shri Bharat N. Doshi

Member

(iv) Shri Sudhir Mankad

Member

(v) Shri Rajiv Kumar

Member

(vi) Shri N.S. Vishwanathan

Member

The terms of reference (ToR) of the Committee are given below:

2.1 Keeping in consideration (i) statutory mandate under Section 47 of the RBI Act that the profits of the RBI shall be transferred to the Government, after making provisions ‘which are usually provided by the bankers’, and (ii) public policy mandate of the RBI, including financial stability considerations, the Expert Committee would:

  1. review status, need and justification of various provisions, reserves and buffers presently provided for by the RBI; and

  2. review global best practices followed by the central banks in making assessment and provisions for risks which central bank balance sheets are subject to;

2.2 To suggest an adequate level of risk provisioning that the RBI needs to maintain;

2.3 To determine whether the RBI is holding provisions, reserves and buffers in surplus / deficit of the required level of such provisions, reserves and buffers;

2.4 To propose a suitable profits distribution policy taking into account all the likely situations of the RBI, including the situations of holding more provisions than required and the RBI holding less provisions than required;

2.5 Any other related matter including treatment of surplus reserves, created out of realized gains, if determined to be held.

The Memorandum of Constitution of the Expert Committee is at Annex I.

3. The Committee held eleven meetings during the course of its deliberations. The first meeting was held on January 8, 2019. As the Committee was required to submit its report within a period of 90 days from the date of its first meeting, an extension was granted by the RBI.

4. These meetings were also attended by Dr. Deepak Mohanty (Executive Director, RBI), Shri Amit Agrawal (Joint Secretary, Department of Financial Services) and Dr. Shashank Saksena (Adviser, Department of Economic Affairs) as special invitees in light of their expertise and long-standing association with the ECF.

5. Shri Rohit P. Das (General Manager, RBI) was the nodal officer to the Committee and provided outstanding secretariat support to the Committee.

6. The Committee expresses its appreciation to Dr. Deepak Mohanty, Shri Amit Agrawal, Dr. Shashank Saksena and Shri Rohit P. Das for the extensive contribution and support provided to the Committee.

7. The Committee expresses its appreciation to the Government officials Dr. C. S. Mohapatra (Additional Secretary, DEA), Shri Abhishek Anand (Deputy Director, DEA), Shri Shubham Bhatia (Officer on Special Duty, DFS) and Ms. Meetu Aggarwal (Officer on Special Duty), who extensively supported the Committee.

8. The Committee records its appreciation to the supporting RBI team comprising of Smt./Shri Minal A. Jain, Saurabh Aggarwal, Kaustubh Jambhulkar, Ashish Gupta, Sangeetha Mathews, Dr. N. K. Unnikrishnan, Dr. D. Bhaumik, Indranil Bhattacharya, Shriti Das, Jaikish, Manoranjan Padhy, Indranil Chakraborty, S. S. Ratanpal, Purnima S. Lakra, Dr. S. Gayen, Dr. Jai Chander, Dr. Saurabh Ghosh, Shailaja Singh, Savitha Rajeevan, Meenakshi S. Seet, Pradeep Kumar and Saket Kumar.

9. The Committee expresses its appreciation to RBI, New Delhi for providing logistic support.

10. The Committee finalized its recommendations after, inter alia, taking an overview of the role of the central bank’s financial resilience, reviewing cross-country practices, and assessing the impact of RBI’s public policy mandate and operating environment on its balance sheet and risks.

11. Finally, the Committee would like to thank Shri Shaktikanta Das (Governor, RBI), for entrusting it with this responsibility.


B Executive Summary

The Expert Committee constituted to review the RBI’s extant ECF, was guided by the principle that the alignment of the objectives of the Government and the RBI is important. As a central bank is a part of the Sovereign, ensuring the credibility of the RBI is as important, if not more, to the Government as it is to the RBI itself. The Committee also noted that while there may occasionally arise a difference of views in the conduct of the central bank’s operations, there always needs to be harmony in the objectives of the Government and the RBI.

In recognition of the fact that the RBI forms the primary bulwark for monetary, financial and external stability, the Committee was of the view that the financial resilience of the RBI needs to be maintained above the level of peer central banks, as would be expected of the central bank of one of the fastest growing economies of the world.

Towards this end, the Committee recommended adopting the Expected Shortfall (ES) methodology (in place of the extant Stressed-Value at Risk) for measuring market risk on which there was growing consensus among central banks as well as commercial banks over the recent years. While central banks are seen to be adopting ES at 99 per cent confidence level (CL), the Committee recommended adoption of a target of ES 99.5 per cent CL and a range defined between the target and downward risk tolerance of 97.5 per cent (both under stress conditions). The range is considered appropriate to address the cyclical volatility of RBI’s valuation balances based on historical analysis.

The Committee recognized that the RBI’s Contingency Risk Buffer (CRB) is, inter alia, the country’s savings for a ‘rainy day’ (a financial stability crisis) which has been consciously maintained with RBI in view of its role as Lender of Last Resort (LoLR). Financial stability risks are those rarest of the rare, fat tail risks whose likelihood can never be ruled out, especially in light of the Global Financial Crisis (GFC) and whose impact can be potentially devastating. Public policy prudence and extant statutory provisions require the RBI to maintain appropriate level of risk buffers for this purpose. The Committee recommended that the same be maintained at a range of 5.5 per cent to 6.5 per cent of the RBI’s balance sheet which is above the available level of 2.4 per cent of balance sheet as on June 30, 2018 (vis-à-vis a target of 3.7 per cent of balance sheet).

Application of these recommendations to RBI’s 2017-18 balance sheet is seen to result in RBI’s risk equity levels in a range of 25.4 per cent to 20.8 per cent of balance sheet which will enable the RBI to retain one of the highest levels of financial resilience among central banks globally.

The Committee recognized that the opportunity cost of RBI’s capital is minimal as the RBI returns a major part of the coupon interest on the Government of India Securities (G-Sec) held against its capital, reserves and risk provisions as surplus transferable to Government. Further, the composition and size of RBI’s balance sheet is determined by public policy considerations and generates positive externalities of fostering monetary and financial stability.

The Committee has recommended a surplus distribution policy which targets not only the total economic capital (as per the extant framework) but also the realized equity level of the RBI’s capital. This will help bring about greater stability of surplus transfer to the Government, with the quantum of the latter depending on balance sheet dynamics as well as the risk equity positioning by the Central Board. There will be no transfer of unrealized valuation buffers and these will be used as risk buffers against market risks.

In view of the above recommendation, the excess realized equity as on June 30, 2018 ranges from ₹ 26,280 crores (at upper bound of CRB) to ₹ 62,456 crores (at lower bound of CRB). The excess realized equity as on June 30, 2019 will need to be determined on the basis of RBI’s finalized annual accounts for the financial year 2018-19 as well as the realized equity level decided upon by the RBI’s Central Board.

The Committee recommends the alignment of the financial year of RBI with the fiscal year of the Government for greater cohesiveness in various projections and publications brought out by RBI. Further, in the following years, interim dividend to the Government may be paid only under exceptional circumstances.

The Committee recommends that the framework may be periodically reviewed every five years. Nevertheless, if there is a significant change in the RBI’s risks and operating environment, an intermediate review may be considered.

1. The Reserve Bank of India (RBI) is one of the pioneers in the area of central bank capital, starting with the Subrahmanyam Group which submitted its report in early 1997. This was followed by the Thorat Committee in 2004 (recommendations of which were not accepted), the Malegam Committee in 2014 (recommendations of which were accepted) and the Economic Capital Framework (ECF) which was developed between 2014 - 2015 and operationalized by the RBI in 2015-16, so as to operate concurrently with the Malegam Committee’s recommendations which were valid for a three-year period, i.e. 2013-14 to 2015-16.

2. This periodic assessment indicates the importance that the Government of India (Government) and the RBI have placed on finding the right balance between the opportunity cost of central bank capital vis-à-vis the socio-economic cost and the negative externalities of having an undercapitalized central bank, making it imperative that a holistic and comprehensive perspective be taken based on what is in the best interest of the country as a whole.

Central bank capital and its role in monetary and financial stability

3. Central banks do not require capital to carry on operations, as being the managers of domestic liquidity, they can do so simply by printing currency/ creating liquidity. The Committee recognised that central banks require financial resilience to absorb the risks that arise from their operations and the delivery of their public policy mandate of buffering the economy from monetary shocks and financial stability headwinds (by virtue of them being the monetary authority as well as LoLR). Emerging Market and Developing Economy (EMDE) central banks have an additional role of managing external stability in the face of volatile capital flows, and the spillover effect of monetary policy changes by Advanced Economies (AE) central banks.

4. The Committee is of the view that there is an important link between central banks’ financial resilience and its policy efficacy. A survey of international literature also reveals that this is the predominant view in the academia and the central banking community.

Central banks’ unique risk environment and their risk management frameworks

5. Central banks are exposed to some similar risks as commercial banks, though their operating risk environment is also unique on account of the following:

  1. Being public policy institutions, central banks’ focus is on ensuring efficacy of their policy actions even if such actions entail assuming significant balance sheet risks. This, in effect, impacts the central banks’ balance sheet and its management significantly.

  2. Central banks may also be required to adopt a ‘counter-intuitive’ approach to risks during crises wherein they relax their risk tolerance limits (RTL) and collateral standards to act as LoLR as well as market maker of last resort (MMLR), precisely at the time when commercial entities are strengthening their risk management standards.

  3. On the other hand, there are certain inherent strengths in a central bank’s balance sheet, i.e. being the creators of domestic liquidity they cannot run out of it even during a crisis. Seigniorage income adds to the strength of the balance sheet and central banks are believed to have the implicit (or, in some cases, explicit) support of the government.

6. Among central banks, given the considerable variation in their roles and responsibilities, the environments they operate in, their financial relationship with the Sovereign and their accounting frameworks, there is no internationally laid down risk capital framework for central banks. Central banks, therefore, develop and adapt risk management frameworks to their own specific conditions and requirements. This also means that international comparisons will only reveal global trends and averages, but not a generally agreed international norm.

7. The broad approach that most central banks have followed is to draw a distinction between risks arising out of monetary policy/ financial stability operations and other risks. Many of the central banks actively monitor the risks arising from their monetary policy operations, but do not seek to limit or offset those risks for reasons relating to policy efficacy, while risks arising from non-monetary operations are actively managed. Institutional mechanisms are put in place to ensure that financial resilience is appropriate to absorb the impact of policy risks.

Review of central banking practices

8. The Committee was informed by a cross-country analysis of 53 central banks and the salient observations are outlined below.

  1. Capital structure: Several leading central banks have adopted holistic risk capital frameworks to assess the adequacy of their reserves and provisions. The RBI’s ECF is in line with this approach.

  2. Risk methodologies: The methodologies adopted by central banks for assessing risks have evolved with the operating environment and the developments in risk assessment. Initially, Value-at-Risk (VaR) was used by central banks, but after the GFC, it has been increasingly supplemented with/ replaced by Stressed Value at Risk (S-VaR) or Expected Shortfall (ES). More recently, ES is emerging as the risk model of choice and the Committee’s recommendation to adopt this model is a move with the times.

  3. Risk transfer mechanisms: While certain central banks (including the RBI) supplement their financial resilience with risk transfer mechanisms (RTM), the efficacy of RTM can be truly assessed only during an actual crisis when the fiscal space available to the government could also get significantly reduced. In view of the same, the preference of a central bank could normally be to expect ex ante capitalization.

  4. Credit ratings of central banks: It was observed that wherever central banks were rated, the credit ratings of central banks which were not a part of any currency union were predominantly at the same level as their respective Sovereigns. It was also observed that the Credit Rating Agencies (CRA) in their assessment of Sovereign ratings assign weightage to areas which generally fall within the purview of central banking operations, i.e., exchange rate management and monetary policy.

Comparison of central banks’ risk buffer levels

9. The Committee noted that the RBI had an overall fifth rank in 2018 at 26.8 per cent of its balance sheet with respect to central banking economic capital, largely emanating from revaluation balances accumulated by rupee depreciation vis-à-vis the US dollar. Among the EMDEs, the RBI’s position was fourth in 2018, with the other concerned central banks also having large revaluation buffers.

10. The RBI’s realized equity (the component which is actually determined by the central bank’s management) was 7.2 per cent of its balance sheet in 2018 as revaluation balances account for 73 per cent of RBI’s economic capital.

11. The Committee noted that drawing definitive conclusions from simple comparative analysis with equity levels of other central banks is difficult because of the following reasons:

  1. A central bank’s economic capital requirements will vary according to its roles and responsibilities, operating environment, reserve currency status, currency convertibility status, exchange rate regime, financial stability responsibilities, accounting frameworks, availability of fiscally credible RTMs, and vulnerabilities on the macroeconomic and financial sector front, etc.

  2. Inter-temporal variations in balance sheet size and the consequent impact on the capital size, e.g. the capital of the US Federal Reserve (US FED) and the Swiss National Bank (SNB) was around 4 per cent and 50 per cent before the GFC which have reduced to about one percent and 16 per cent, respectively.

  3. During periods of stress and currency depreciation, the revaluation balances of central banks typically go up which is not truly reflective of financial resilience.

  4. Negative equity central banks cannot be reckoned in arriving at an estimate of target level of equity since they tend to reduce the measure of central tendency. Such central banks may be treated as exceptions as there are not many negative equity central banks.

The RBI’s public policy mandate and their impact on its balance sheet and risks

12. The RBI is a full service central bank. Among its varied functions, the role of monetary authority, forex reserve management and fostering of financial stability can particularly give rise to balance sheet and contingent risks for the RBI. The most significant impact of public policy considerations on the RBI’s balance sheet is the size of the forex reserves maintained to manage the volatility in the exchange rate. While these reserves provide the economy with a buffer against external stress, they give rise to significant risks for the RBI, as they have to be maintained as open, unhedged positions thereby exposing the RBI to currency risk on more than three-fourths of its balance sheet. In the past, mark-to-market (MTM) losses of 1.1 to 1.5 per cent of the gross domestic product (GDP) have been experienced during certain periods. Moreover, the materialization of sterilization risks has caused large variability in RBI’s surplus during years of strong foreign inflows, when the balance sheet is already under strain due to the MTM losses. Nevertheless, the RBI has never suffered an overall loss in any year.

RBI’s rationale for risk parameterization

13. As part of the review of the extant ECF, the Committee took into consideration the RBI’s rationale for risk parameterization:

(i) The RBI had adopted the then prevailing Basel methodologies for market, credit and operational risks as these represented the most widely accepted risk assessment methodologies. At the time of adoption, the S-VaR represented the latest risk management standard as it was introduced globally in 2009 by the Basel Committee on Banking Supervision (BCBS) in the aftermath of the GFC to address the limitations observed in the VaR methodology during the crisis. Other leading central banks were seen to be using this approach at that point of time. The actual risk parameterization of the ECF - return period, time horizon, size of data set, distribution assumptions, components of economic capital, etc. was carried out keeping in mind RBI-specific considerations.

(ii) The 99.99 per cent CL was selected in recognition of the fact that the RBI is the external face (international counterparty) of the Government and also forms the primary bulwark during external crises for which it requires financial resilience to match the highest credit rating in international markets in light of the following:

  1. The country’s EMDE status.

  2. Rising vulnerabilities associated with a progressively open capital account, global spillovers, volatility of markets and capital flows.

  3. These vulnerabilities being aggravated by India’s persistent twin (current account and fiscal) deficits.

  4. The lack of flexibility on the external front due to the rupee not being a reserve currency.

  5. The need to ensure credibility of RBI’s policy actions by being able to bear the risks and costs of these actions on its own.

(iii) The objective of RBI having the financial resilience to match the highest credit rating in international markets was to be seen as an unimpeachable counterparty in international transactions and convey its ‘creditworthiness’ to the external sector, even during times of crises. (The importance of financial resilience can be seen as an important learning from the success of the FCNR (B) swap scheme during the Taper Tantrum of 2013);

(iv) The financial stability risks are those rarest of the rare, fat tail risks whose likelihood can never be ruled out and whose impact can be potentially devastating. The ECF takes cognizance of the fact that emergency liquidity assistance (ELA) operations would be riskier in banking sectors with high non-performing asset (NPA) levels. The NPA crisis has thrown light on the challenges that arise if a sizable majority of the banking sector needs to be recapitalized during a financial stability crisis. This necessitates the need for RBI’s balance sheet to be demonstrably credible to discharge the LoLR function.

The extant ECF-SSDP and risk provisioning

14. The Committee, thereafter, reviewed the trends in RBI’s surplus distribution under the ECF-SSDP framework from a historical perspective, as well as in comparison with other central banks. In this regard, the Committee noted the following:

  1. The risk provisioning by RBI, as a percentage of total net income, has come down from around 50 per cent earlier to 10 per cent since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP. The RBI has transferred ₹2.65 trillion (90 per cent of its net income) to the Government over the same period.

  2. At 90 per cent transfer of net income to the Government, the ECF-SSDP compares well with other central banks.

  3. The practice of paying interim dividend commenced in 2016–17.

  4. While the RBI does not calculate seigniorage income, the surplus transferred over the years has been substantially higher than the seigniorage income, as the Issue Department balance sheet, historically, accounts for only around 50 per cent of the RBI’s balance sheet.

  5. RBI’s surplus distribution since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP compared favourably with other EMDEs which have even higher economic capital levels than the RBI due to their currency depreciation.

Quality of RBI’s risk buffers

15. Consequent to the transfer of surplus as indicated above, the RBI’s realized equity (Capital, Reserve Fund, Contingency Fund [CF] and Asset Development Fund [ADF]) as a proportion of balance sheet is at similar levels as in the late 1990s, though significant amount of unrealized revaluation balances are now available to act as risk buffers against market risks.

16. The RBI’s economic capital has also undergone a significant transformation over the past 20 years, with the unrealized revaluation balances now accounting for almost 73 per cent of the RBI’s economic capital in 2017-18 vis-à-vis 37.9 per cent in 1997.

The Committee’s observations and recommendations

17. The Committee reviewed the extant ECF and its associated SSDP. The Committee has made the following observations/ recommendations.

Economic capital levels

18. The Committee observed that even if the RBI’s economic capital could appear to be relatively higher, it is largely on account of the revaluation balances which are determined by exogenous factors such as market prices, and the RBI’s discharge of its public policy objectives. The proportion of realized equity to balance sheet has come down through the surplus distribution – balance-sheet expansion adjustment process since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP.

Review of status, need and justification of RBI’s buffers

19. The status, need and justification of various reserves, risk provisions and buffers maintained by the RBI were reviewed by the Committee, which recommended their continuance. The Committee recommended that the RBI should explicitly recognize the ADF not only as a provision for capital expenditure but also as a risk provision in case of need.

Treatment of revaluation balances

20. The Committee recommended the inclusion of the revaluation balances as a part of RBI’s overall risk buffers, but with the recognition of its special character in view of their volatility, limited usability, significant strategic and operational constraints on their monetization. The principles of non-distribution of revaluation balances, mapping these only against market risks, and one-way fungibility vis-à-vis realized equity would need to be continued.

Transparency in accounts

21. In view of the distinction sought to be made between realized equity and revaluation balances, the Committee recommended a more transparent presentation of the RBI’s Annual Accounts with regard to the components of economic capital (Table E.1).

Table E.1: Extant and suggested presentation of the liability side of RBI’s balance sheet
Existing liabilities format Proposed liabilities format
  • Capital

  • Reserve Fund

  • Other Reserves

  • Deposits

  • Other Liabilities and Provisions

  • Notes in Circulation

  • Capital

  • Reserve Fund

  • Other Reserves

  • Risk Provisions

    • Contingency Fund

    • Asset Development Fund

  • Revaluation Accounts

  • Deposits

  • Other Liabilities

  • Notes in Circulation

Articulation of financial resilience of the RBI

22. Going forward, the desired financial resilience for the RBI may be articulated by the Central Board in terms of the risk protection desired for its balance sheet.

Selection of the risk model to be used

23. Given that ES is a better risk measure for tail risk as well as a coherent risk measure unlike VaR and S-VaR and that there is an increasing convergence on the use of ES, adoption of the ES methodology for the RBI’s market risk provisioning was recommended.

Selection of risk parameters

24. Keeping in view, the historical incidence of stress and the need to maintain high level of financial resilience for RBI as well as to take into account the volatility and cyclicality in revaluation balances, the Committee considered various alternate risk parameterizations and selected the ES 99.5 per cent CL under stress conditions as the target resilience for market risk. The Committee noted that this was higher than other central banks who were seen to be using ES 99. The Committee also articulated a risk tolerance limit of ES 97.5 per cent CL based on historical analysis to impart the necessary flexibility to account for the cyclical volatility in RBI’s valuation buffers. Risk provisioning to cover shortfall in market risk would be triggered only if the tolerance limit of ES at 97.5 per cent CL is breached.

25. The Committee was also of the view that even when capital flows and the rupee are strong, government finances buoyant and the country prospering, the RBI will need to have adequate financial resilience to absorb the risks of the challenging monetary policy conditions which would arise in such a scenario caused by large inflows.

Assessing off-balance sheet exposures

26. The RBI should assess the risk of its off-balance sheet exposures in view of their increasing significance.

The country’s rainy-day savings

27. The Committee recognized that the RBI’s financial stability risk provisions need to be viewed for what they truly are, i.e., the country’s savings for a rainy day (a financial stability crisis), built up over decades, and maintained with the RBI in view of its role as the LoLR. Its balance sheet, therefore, has to be demonstrably credible to discharge this function with the requisite financial strength.

Assessing financial stability risks

28. Globally, central banks are seen to be key custodians of financial stability. While they are known to use scenario analysis to assess risks arising from such actions, this is an area where most central banks, including the RBI, are relatively more discreet because of the associated moral hazard in spelling it out upfront. In India, the position of law is such that the RBI is not only the monetary authority, but also the regulator and supervisor, inter alia, of commercial banks, NBFCs and payment systems, and the debt manager of the Government. The Committee agreed that the RBI has one of the widest financial stability mandates deeply entrenched in the RBI’s statute and it is also bound by Section 47 of the RBI Act, 1934 to maintain the financial resources commensurate with the task. While the potentially destabilizing events have been skilfully handled through successful mergers, acquisitions and recapitalization in the past, the Committee acknowledged that the possibility of financial stability risks materializing can never be ruled out, especially in view of the lessons learnt from the GFC.

29. Given that the Government’s manoeuvrability on recapitalization of commercial banks or of the RBI could be constrained during a financial stability crisis, the Committee recognized the need for the RBI to maintain adequate risk buffers to ensure appropriate level of financial resilience in such circumstances.

30. The assessment made in the initial implementation stages of the extant ECF using peak liquidity scenario analysis had suggested that this risk buffer should be between 2 to 6.5 per cent of the RBI’s balance sheet. In light of the same, the Central Board had previously decided to maintain the buffer at 3 per cent with a medium-to-long term target of 4 per cent of the balance sheet. The Committee was also informed by a separate scenario analysis to assess the RBI’s ELA requirements using the European Central Bank’s (ECB) methodology for the liquidity stress-testing of commercial banks under its jurisdiction. Thereafter, a recovery rate ranging from 60 percent to 80 percent on the ELA was applied to estimate the RBI’s LoLR risks. The Committee considered the scenario of ELA to top 10 commercial banks with an 80 per cent recovery rate which results in a risk estimate of 4.6 per cent of the balance sheet. This analysis did not take into consideration the interconnectedness in the financial sector, the risks arising out of Indian banks’ overseas operations or the risks arising from the Deposit Insurance and Credit Guarantee Corporation (DICGC) which is a wholly-owned subsidiary of the RBI. In light of the above, the Committee recommended that the size of the financial and monetary stability risk provisions should be maintained at 4.5 to 5.5 per cent of the balance sheet. The scale of provisioning was moderate when assessed against the scale of costs of financial stability crises globally.

Monetary stability risks

31. The CRB represents the cushion for both financial stability as well as monetary stability risks in view of their low correlation.

Assessing credit and operational risks

32. The Committee recommended the adoption of the Basel III Standardised Approach for assessing credit risk of the forex portfolio (which also covers off-balance sheet exposures) and the new Standardised Approach for operational risk.

Joint credit-market risk modelling

33. The RBI should consider joint credit-market risk modelling as this would help simulate the combined impact of a crisis and may lead to lower risk provisioning due to diversification.

Size of realized equity

34. This should cover the requirements of the CRB (i.e., sum of credit risk, operational risk, and financial and monetary stability risks) as well as any shortfall in revaluation balances vis-à-vis the market RTL. Given that, as on June 30, 2018, there was no shortfall in revaluation balances, the size of the realized equity should be 6.5 per cent of the balance sheet, with a lower bound of 5.5 per cent. This represents 1.2 to 1.4 per cent of the GDP.

35. The net position of the risk provisions as determined by applying the recommendations of the Committee is summarized in Table E.2. Application of the Committee’s recommendations to the RBI’s balance sheet for the year 2017-18 results in excess revaluation balances of 0.7 per cent of balance sheet and excess realized equity ranging from 0.7 per cent at the upper bound of CRB to 1.7 per cent of balance sheet at the lower bound of CRB.

Table E.2: Risk provisions as per extant and proposed ECF (June 30, 2018)
  Extant ECF Proposed ECF
Available risk buffers Required risk buffers Net position Available risk buffers Required risk buffers Net position Excess
Market risk 19.6* plus 4.8** 24.4 - 19.6 18.9
{RTL: 15.3}
(+) 0.7 VB: 0.7
Financial & monetary stability risk 1.7 3
[medium term target: 4]
(-) 1.3
[(-) 2.3]
6.3 4.5 to 5.5 (+) 0.8 to (+) 1.8 RE: 0.8 to 1.8
Credit risk 0.4 0.4 - 0.6 0.6 - -
Op risk 0.3 0.3 - 0.3 0.3 - -
Total risks/ risk buffers 26.8 28.1
[29.1]
(-) 1.3
[(-) 2.3]
26.8 20.8 to 25.4 # (+) 1.5 to (+) 2.5^ VB: 0.7+ RE: 0.7 to 1.7#
* VB: Revaluation balances ** RE: Realized equity ^ Excess is in the form of 0.7 per cent revaluation balances and 0.8 to 1.8 per cent realized equity. {}: Risk Tolerance Limit
# As the lowest estimate of RBI’s LoLR risk is 4.6 per cent (Table 4.9) and the sum of credit and operational risk is 0.9 per cent, the lower bound of the CRB is to be maintained at 5.5 per cent with an upper bound of 6.5 per cent. Consequently, the excess RE is 0.7 to 1.7 per cent.

Treatment of excess realized equity

36. The excess realized equity as on June 30, 2018 ranges from ₹ 26,280 crores (at upper bound of CRB) to ₹ 62,456 crores (at lower bound of CRB). The excess realized equity as on June 30, 2019 will need to be determined on the basis of RBI’s finalized annual accounts for the financial year 2018-19 as well as the realized equity level decided upon by the RBI’s Central Board.

Opportunity cost of RBI’s capital

37. The Committee was also of the view that the return/ cost of RBI’s capital, which is held for public policy objectives involves considerable positive externalities. If these do need to be assessed, it may be done on two broad principles viz. (i) the difference in the overall return on the assets held and the average debt servicing cost of the Government and (ii) the opportunity cost of capital which is the return that the Government would have generated had RBI’s capital been redeployed. With regard to overall return, the assets held against risks buffers could include both a portion of the Net Foreign Assets (NFA) and the Net Domestic Assets (NDA), depending on the composition of the RBI’s balance sheet at any given time. On NDA, RBI receives coupon interest on the G-sec it holds, which is predominantly returned to the Government in the form of surplus transfers. On NFA, the coupon returns may be lower than on NDA, but are typically augmented by valuation returns that accrue to the revaluation balances. The positive impact of NFA on the sovereign rating reduces Government’s overall borrowing costs, and hence has an indirect pecuniary benefit.

38. With regard to the opportunity cost of RBI’s capital and retained earnings, given that G-sec are held against it, the fiscal impact of RBI’s realized equity is minimal1 as RBI predominantly returns the coupon received on the G-sec. Further, given the large size of India’s GDP, the transfer of RBI’s ‘excess’ capital will not have a material impact on its debt-GDP ratio, while negatively impacting other rating criteria used by the CRAs. With regard to the possibility of the debt held against central bank’s capital crowding out the private sector borrowings, the Committee noted that Meyer (2000) had observed that government debt held by the private sector is not affected by the existence or the level of the surplus held by central banks. The opportunity cost of RBI’s capital is, thus, seen to be relatively small, even without taking into consideration the positive externalities of monetary and financial stability which these buffers facilitate.

The Surplus Distribution Policy going forward

39. The surplus distribution policy (SDP) should move away from targeting total economic capital alone (as under the extant SSDP), to one where it has a dual set of targets:

  1. The total economic capital of the RBI; and

  2. The level at which realized equity is to be maintained.

40. Given that market risk was mapped against revaluation balances and only a shortfall in these balances needs to be provided for, the SDP, in effect, will be required to target the required level of realized equity (‘requirement’) for covering:

  1. monetary and financial stability risks

  2. credit risk

  3. operational risk

  4. A shortfall, if any, in revaluation balances vis-à-vis market risk RTL.

41. The ‘available realized equity’ (ARE), i.e., Capital, Reserve Fund, CF and ADF, will be compared with the ‘requirement’ to determine surplus distribution on the following lines:

  1. Entire net income shall be transferred to the Government, if the RBI’s ARE is equal to or greater than upper bound of the ‘requirement’.

  2. Subject to ARE lying within the range of ‘requirement’, the Central Board may consider risk provisioning in a manner so as to maintain the RBI’s ARE within the range of ‘requirement’, till the next periodic review.

  3. If the ARE falls short of the lower bound of ‘requirement’, appropriate risk provisioning should be made by the RBI to augment the realized equity to the lower bound of ‘requirement’ and only the residual net income (if any) should be transferred to the Government.

  4. If any risk provisioning from net income has been made previously for market risk, the excess realized risk provisioning over the target level of market risk buffers (ES 99.5 stress), caused by an increase in revaluation balances, may be reversed.

  5. There shall be no distribution of unrealized revaluation balances.

Consistency in the level of risk provisioning

42. The Committee noted that on making reasonable allowance for volatility (± 0.5 SD and ± 1 SD) in the RBI’s net income relative to its balance sheet size, average risk provisioning over the five year period of 2018-19 to 2022-23 for CRB of 5.5 and 6.5 per cent could range from 8.1 to 16.6 per cent of net income in the normal scenario with a range of 5.4 to 11.1 per cent of net income in case of a positive shock and 16.0 to 32.8 per cent of net income in case of a negative shock respectively. The Committee also noted that these were illustrative and not exhaustive scenarios.

Treatment of excess revaluation balances

43. The Committee was of the view that it should not concern itself with the issue of alternative deployment of excess accumulated revaluation balances as it did not fall within the Committee’s ToRs. The Committee recommended that these may continue to remain on the balance sheet till such time that they may be realized through the sale or maturity of the underlying asset.

Interim dividend and aligning RBI’s financial year with the Government’s fiscal year

44. The Committee recommended that the RBI accounting year (July to June) may be brought in sync with the fiscal year (April to March) from the financial year 2020-21 for the following reasons:

  1. The RBI would be able to provide better estimates of the projected surplus transfers to the Government for the financial year for budgeting purposes;

  2. It could reduce the need for interim dividend being paid by the RBI. The payment of interim dividend may then be restricted to extraordinary circumstances;

  3. It would obviate any timing considerations that may enter into the selection of open market operations (OMO)/ Market Stabilization Scheme (MSS) as monetary policy tools; and

  4. It would also bring about greater cohesiveness in the monetary policy projections and reports published by the RBI which mostly use the fiscal year as the base.

Periodicity of review

45. The Committee recommended that the framework may be periodically reviewed every five years. Nevertheless, if there is a significant change in the RBI’s risks and operating environment, an intermediate review may be considered.


1 An Overview of the Role and Relevance of Central Banks’ Financial Resilience

1.1 The RBI is one of the pioneers in the area of central bank capital, starting with the Subrahmanyam Internal Working Group which submitted its report in early 1997. This preceded the publication of Dr. Peter Stella’s seminal paper ‘Do Central Banks Need Capital’ (Stella, 1997), which subsequently triggered considerable research in this area. This was also before the creation of the European System of Central Banks (ESCB) in 1998 – a framework which explicitly laid emphasis on the financial resilience of its member central banks as a means of ensuring their functional independence.

1.2 The Subrahmanyam Group was followed by the Usha Thorat Committee in 2004 (recommendations of which were not accepted), the Malegam Committee in 2014 (recommendations of which were accepted) and the ECF which was developed during 2014-15 and operationalized by the RBI in 2015-16, so as to operate concurrently with the Malegam Committee’s recommendations which were valid for a three-year period, i.e., 2013-14 to 2015-16.

1.3 Given that the role and adequacy of central bank capital is an issue which generally receives greater attention only during crises, the continued attention on this issue in India reveals the importance that the Government and the RBI have placed on finding the right balance between the opportunity cost of central bank capital vis-à-vis the socio-economic cost and the negative externalities of having an undercapitalized central bank, making it imperative that a holistic and comprehensive perspective be taken based on what is in the best interests of the country as a whole. The challenge in finding this right balance arises primarily from the fact that the opportunity cost of central bank capital is relatively easier to measure than the benefits of having a well-capitalized central bank for fostering ‘monetary and financial stability’, given that these are a public good and, therefore, difficult to measure during normal times.

I. Central bank capital and its role in monetary and financial stability

1.4 Central banks do not require capital to carry on operations, as being the managers of domestic liquidity they can do so simply by printing currency/creating liquidity. However, central banks require financial resilience2 to absorb the risks that arise from their operations and delivery of their public policy mandate.3 To fully appreciate the importance of the same, one needs to view central banks as macro-level risk managers, mandated with the public policy objective of buffering the economy from monetary shocks and financial stability headwinds (by virtue of they being the monetary authority as well as the LoLR). Emerging market central banks have an additional role of managing external stability in the face of volatile capital flows and the spillover effect of monetary policy changes by AE central banks. The role of central banks’ financial resilience is to enable these institutions to focus on their primary function of fostering monetary, financial and external stability, even in the midst of crisis, without being diverted by balance sheet concerns. This is particularly important given that central bank capital generally represents public resources and the central bank’s management can be held accountable for its losses.

II. Survey of literature

1.5 There are varied views on the role of central banks’ capital/financial resilience. On the issue of central banks being able to carry on operations even with negative capital, Stella and Lönnberg (2008) drew a distinction between ‘technical insolvency’ and ‘policy insolvency’, i.e., a central bank may be able to carry on day-to-day operations with negative equity but may not be effective in the implementation of its policy objectives. Adler, Castro, and Tovar (2016), Klüh and Stella (2008), and Perera, Ralston, and Wickramanayake (2013) had observed a negative relationship between central banks with weak financial resilience and the discharge of their policy mandate. Dalton and Dziobek (2005) concluded that failure to address ongoing losses, or any ensuing negative net worth, will interfere with monetary management and may jeopardize the central bank’s independence and credibility. Sims (2013) also concluded that the LoLR role of the central bank may not be credible if the central bank equity position is not strong.

1.6 Bindseil, Manzanares, and Weller (2004) found that as a fully automated and credible rule of recapitalization of the central bank by the government is difficult to implement in practice, positive capital4 seems to remain a key tool in ensuring that independent central bankers always concentrate on price stability in their monetary policy decisions. Archer and Moser-Böehm (2013) observed that the mere act of seeking recapitalization from the government might cause central banks to give up an authority that had been purposefully delegated to them.​

1.7 Specifically, Friedman and Schwartz (1963) observed that the US FED’s concern about its own balance sheet weighed on the decision which prevented an aggressive monetary expansionary response to the emerging Great Depression. Krugman (1998) and Cargill (2005) have argued that Bank of Japan (BoJ) committed similar policy errors as it was concerned with its net worth position. Amador et al. (2016) observed that the dilemma between the desire to maintain currency pegs and the concern about future losses can lead the central bank to first accumulate a large amount of reserves, and then to abandon the peg, as observed in the Swiss case. Hall and Reis (2015) arrived at a similar conclusion.

1.8 On the other hand, according to Subramanian et al. (2018), central banks can always deliver on their domestic operations regardless of their net worth because they can always issue liabilities (‘print money’); and that central banks are a part of the government, hence it is the broader government balance sheet that matters, not that of any of its constituents. In this regard, Buiter (2008), states that a central bank’s balance sheet is uninformative about the financial resources it has at its disposal and about its ability to act as an effective LoLR and MMLR, and, therefore, the equitable insolvency (the failure to pay obligations as they fall due) is more relevant for central banks than balance sheet insolvency, i.e., liabilities exceeding assets. He, however, noted that the scale of recourse to seigniorage to safeguard central bank solvency may undermine price stability. Benecka et al. (2012) did not find any significant link between central bank financial strength and inflation. Frait (2005) as well as Dalton and Dziobek (2005) sought to differentiate between central banks with operating losses from those with valuation losses caused by currency appreciation. Ernhagen, Vesterlund, Viotti (2002) broadly agreed that as long as overall conditions are reasonable, the ‘seigniorage’ income of a central bank will add to the financial strength of the central bank. A central bank would be able to ensure its solvency through seigniorage as long as it does not have significant foreign exchange-denominated liabilities or index-linked liabilities. For these reasons, a number of central banks such as those of Israel, Chile, the Czech Republic and Mexico have continued to operate quite successfully for long periods with negative capital. Restrepo et al (2008), on the other hand, in relation to the Chilean case, observed that it would take at least 25 years for its net worth to reach positive levels, with a high chance of it being negative equity even after 25 years.

1.9 In this regard, an EMDE central bank which is one of the most cited examples of an effective central bank despite having negative equity over a prolonged period, cited the following reasons for central banks to maintain sufficient capital in its 2006 annual report, while mentioning that the International Monetary Fund (IMF) has for several years recommended its recapitalization and that risk-rating agencies mention the central bank’s negative capital as something that should be corrected:

  1. If a central bank enjoys healthy capitalization, the market will consider it financially fit to act and meet its policy goals and deal with any unforeseen occurrences.

  2. In contrast, if a central bank is perceived as suffering from weak equity, raising concern about the effects this could have on decisions and therefore financial statements, it could lead to loss of credibility and policies becoming less effective. Credibility is important because it enhances the stabilizing effect of monetary policy.

  3. A central bank’s financial independence is necessary to safeguard the technical nature of its decisions. Autonomy could be seriously hurt if a central bank had to urgently request resources from the General Treasury, especially to deal with financial or BoP crises. (The central bank’s current credibility and sound reputation ensure that it will be able to fulfil its duties.)

  4. A well-capitalized central bank reduces the risk of having to issue money to finance itself amidst instability (e.g., to meet its obligations as LoLR). Thus, the country and the central bank are better prepared to deal with a range of critical situations.

1.10 The Committee noted that the aforementioned central bank continues to operate with negative equity as a recapitalization programme launched in 2006 could not be completed in 2009 due to a worsening of the government’s fiscal position.

1.11 With regard to RBI specifically, Subramanian et al. (2018), using the approaches of ‘modal’ risk parameterization and regression analysis, concluded that it is overcapitalized by 13 to 22 percentage points. Similar conclusions were drawn in the Economic Survey 2016–17 and Economic Survey 2017–18. Lahiri et al. (2018), on the other hand, concluded that the RBI was undercapitalized by 5 per cent compared to the average of emerging economies.

III. Central banks’ unique risk environment and their risk management frameworks

1.12 Even though central banks are exposed to some similar risks as commercial banks, i.e., policy and strategic risk, market risk, credit risk, liquidity risk (at least, on forex reserves), information security risk, operational risk, reputation risk, etc., their operating environments are rather unique, resulting in a need for adopting risk management frameworks which are specifically adapted to their environment and public policy mandate:

(i) Being public policy institutions, the focus of central banks is on ensuring efficacy of their policy actions even if such actions entail assuming significant balance sheet risks—an approach which is referred to within the RBI as the Principle of Public Policy Predominance (PPPP).

(ii) This principle impacts the central bank balance sheet and its management significantly. For instance, common risk management tools such as hedging may not be available to central banks and risk-return considerations will figure low in priority in important decisions such as balance sheet composition (the size of forex reserves being more of a strategic decision), keeping the forex reserves as an open position (as they need to be available for intervention purposes), the absence of duration management for the domestic securities portfolio (as it could impact monetary policy operations), etc.

(iii) Some of the largest risks, i.e., monetary and financial stability risks, are specific to central banks and they have been seen to materialize at scales which account for a significant portion of an economy’s GDP. If these risks do indeed materialize and lead to a situation where central banks need recapitalization support, the ability to conduct monetary policy may get eroded, thereby constraining their independence. Moreover, given their scale of operations, central banks are difficult to recapitalize as evidenced by several central banks which operate with negative capital.​

(iv) Given their public policy objectives, central banks may also be required to adopt a ‘counter-intuitive’ approach to risks during crises, wherein they relax their RTL and collateral standards to act as LoLR as well as MMLR, precisely at the time when commercial entities are strengthening their risk management standards.

(v) On the other hand, there are certain inherent strengths in a central bank’s balance sheet which are:

    1. Being the creators of domestic liquidity, central banks cannot run out of it even during a crisis. They thus cannot become ‘technically’ insolvent. (While a commercial bank may be faced with liquidity stress, due to various triggers such as asset-liability mismatch, materialization of other risks, contagion, etc., a central bank under similar circumstances will always be able to carry out operations without disruption by printing currency/creating liquidity). However, this approach may not only compromise their monetary policy objectives, but being the providers of domestic liquidity also brings with it the responsibility of being the LoLR and its own attendant risks;

    2. Central banks earn ‘seigniorage income’5 from their delegated role as issuer of currency which adds to their financial resilience, unless it is predominantly transferred to the government;

    3. Central banks are seen to have the implicit (and, in some cases, explicit) support of the Sovereign.

1.13 Given that roles and responsibilities of central banks vary considerably, as do the environments they operate in, their financial relationship with the Sovereign (RTMs and surplus distribution policies) and their accounting frameworks, there is no internationally laid down risk capital framework for central banks. Central banks, therefore, develop and adapt risk management frameworks to their own specific conditions and requirements. This also means that international comparisons will only reveal international trends and averages but not a generally agreed international norm. Nevertheless, the broad approach that most central banks have followed for developing their risk frameworks is along the following lines:

  1. A distinction is drawn between risks arising out of monetary policy/financial stability operations and other risks.

  2. Many of the central banks actively monitor the risks arising from monetary policy operations, but do not seek to limit or offset those risks for policy reasons.

  3. Non-monetary operations risks (forex reserve risks, operational risks, etc.) are actively managed.

  4. Institutional mechanisms are put in place to ensure financial resilience is appropriate to absorb the impact of policy risks through adequate equity (economic capital)6/ RTMs/ profit transfer mechanisms.

1.14 In the following chapter, international practices adopted by central banks with regard to risk management as well as economic capital and financial resilience are examined.

2 Review of Central Banking Practices

2.1 ‘Economic capital is defined as the methods or practices that allow banks to consistently assess risk and attribute capital to cover the economic effects of risk-taking activities’ (Bank for International Settlements [BIS], 2009). Prior to the development of its own ECF, the RBI conducted a cross-country survey of the frameworks used by 36 leading advanced and emerging economy central banks. The purpose of this exercise was to evaluate the frameworks used by other central banks to assess their own risk capital and provisioning requirements. This was further supplemented by technical workshops held with the BIS and the ECB as well as detailed discussions with Banco Central do Brasil (BCdB), Bank of England (BoE), BNM, Reserve Bank of Australia (RBA), Reserve Bank of New Zealand (RBNZ), South African Reserve Bank (SARB), and Sveriges Riksbank amongst others. More recently, the Government has also conducted a survey of 51 central banks with regard to their total equity levels and risk models used. The Committee was informed by the findings of both these surveys, as well as an extended analysis of 53 central banks (covering all the central banks by the Government and the RBI) on their economic capital, realized equity and other sources of financial resilience and their relative position with regard to macroeconomic and financial stability indicators.

I. Various approaches towards strengthening the central banks’ financial resilience

2.2 Archer and Moser-Böehm (2013) identified capital targets, accounting policies, risk-sharing arrangements, profit distribution and recapitalization mechanisms as key determinants of central bank financial strength. Interestingly, following the GFC, a number of leading central banks strengthened their financial resilience by adopting at least one of these measures as brought out in Box 2.1.

Box 2.1: Different ways central banks strengthened their financial resilience following the Global Financial Crisis

(i) The Monetary Authority of Singapore (MAS) increased its capital by SGD 8 billion to SGD 25 billion in March 2012.

(ii) In 2009, the SNB doubled the provisioning requirements to equal double of the average nominal economic growth rate. In 2016, a minimum annual allocation of 8 per cent of the provisions was further stipulated.

(iii) The Australian government in 2013-14 increased the RBA’s Reserve Fund (treated as its capital) from 3.6 per cent of assets at risk to 15.7 per cent of assets at risk.

(iv) The ECB increased its subscribed capital from €5.76 bn to €10.76 bn in 2010. Its reserves which are capped at the level of capital also increased accordingly. The paid-up capital presently stands at € 7.74 bn.7,8

(v) The Bank of Korea Act was amended in 2011 and the amount to be allocated to reserves was increased from 10 per cent to 30 per cent of net profit.

(vi) The BoE’s Quantitative Easing (QE) is undertaken by a subsidiary of the central bank with the risk-return being transferred to the Treasury.

(vii) The US FED introduced an accounting change wherein losses would be treated as an intangible asset to be recovered before transfers to the Treasury recommence.

2.3 Accordingly, the survey sought to identify key central banking practices in this regard, which are discussed below:

(i) Capital Structure: The amount of central bank capital is generally stipulated by their respective statutes, while reserves/ risk provisions are seen to be the dynamic components of a central bank’s capital structure, changing over time and circumstances. It was observed that several leading central banks, e.g. BoE, ECB, RBA and RBNZ, have adopted holistic risk capital frameworks to assess the adequacy of their reserves and provisions. The RBI’s ECF is, thus, in line with current central banking practices. The salient features of BoE, ECB, RBA and RBNZ’s capital frameworks are presented in Annex III. Other than these, there are a number of other central banks which use targeted levels of reserves/ risk provisions such as the Banque de France (BdF), BoJ, US FED, Norges Bank and the SNB, amongst others. The targeted levels of reserves/ risk provisions of these central banks are also given in Annex III.

(ii) Evolution of risk methodologies: The survey also brings out the fact that central banks are increasingly adopting a model-based approach for assessing risks and that these risk methodologies evolve with the operating environment and the developments in risk assessment. Table 2.1 shows that a number of central banks had started adopting VaR for risk management/capital purposes well before the GFC. However, the crisis revealed severe shortcomings of the VaR (Crotty, 2007; Gopalkrishna, 2013) and central banks strengthened their risk frameworks with the BoE9, RBNZ and the RBI adopting the S-VaR methodology which was prescribed by the BCBS to replace VaR for commercial banks in 2009. 10 A number of other central banks started moving to ES, which has been prescribed by the BCBS to replace S-VaR in 2016. While the risk parameters range from VaR 95 per cent (Hong Kong), S-VaR 99.9 per cent (New Zealand), S-VaR 99.99 per cent (India) to ES 99 per cent (ECB), etc., ES 99 per cent appears to be emerging as the risk parameter of choice among several central banks presently.

Table 2.1: Risk methodologies adopted by central banks

S. No. Country Risk Methodology
1. Australia Stress test and historical analysis replaced VaR in 2017.
2. Austria ES (99 per cent) introduced in 2012. VaR also used.
3. Belgium ES introduced in 2015. VaR also used.
4. Canada Scenario-based stress tests augments VaR.
5. Chile VaR
6. Denmark ES
7. ECB ES (99 per cent) main measure since 2017. VaR also used.
8. Finland ES (99 per cent) introduced in 2016. VaR also used.
9. Germany ES augmented VaR
10. Hong Kong VaR 95 per cent since 2005.
11. India S-VaR 99.99 per cent adopted in 2015.
12. Italy ES augmented VaR
13. Norway ES
14. Netherlands ES in 2012. Scenario analysis also used.
15. New Zealand VaR/ S-VaR at 99.9 per cent adopted in 2014.
16. Poland VaR
17. Spain VaR 99 per cent to 99.9 per cent.
18. Sweden VaR
19. United Kingdom Stress tests replaced S-VaR in 2017.
20. Basel norms for commercial banks/ BIS BCBS prescribes ES 97.5 per cent in place of S-VaR 99 per cent in 2016 (which itself replaced VaR in 2009). BIS, itself, uses 99.995 per cent S-VaR since 2010–11.

(iii) Risk transfer mechanisms: Certain central banks (including the RBI which has the MSS) supplement their financial resilience with RTMs with the government which are detailed in Annex IV. These RTMs include setting up of Special Purpose Vehicles (SPVs) where the risk return of quasi-fiscal actions are transferred directly to the government; the direct transfer of losses exceeding the available reserves to the government; making accounting changes whereby central bank losses are treated as a future claim on the government; and ad hoc measures such as issue of bonds by the governments to the central banks to cover their losses.

(iv) Efficacy of RTMs: The efficacy of RTMs can truly be assessed only during an actual crisis when the fiscal space available to the government could also get significantly reduced. Post-GFC developments have shown that sovereign debt crises can be quickly triggered when large-scale public sector actions are initiated. There are other specific instances where RTMs have been less effective than initially expected. During the Asian Crisis, an East Asian government issued inflation-indexed government bonds (amounting to around 16 per cent of GDP) to its central bank in exchange for the latter’s claims on banks arising due to the liquidity assistance extended by it. However, the bonds were restructured in tranches prior to any payment being made thereon by the government so as to yield 0.1/1.0 per cent with no fixed repayment date/ 20 years maturity. Incidentally, the stipulation that a charge be paid by the government to the central bank should the central bank’s ratio of capital to monetary obligations fall below 3 per cent was abolished in 2011. There have been other instances where recapitalization of central banks has been done through non-interest bearing bonds. In view of the same, the preference of a central bank could normally be to expect ex ante capitalization. Even in the case of a Asian central bank, which has statutory provision for automatic (ex post) recapitalization, surplus ranging from 41 per cent to 59 per cent was transferred (ex ante) to the risk reserves during 2008–2010, which was higher than the required level of 10 per cent. This requirement has since been raised to 30 per cent. The recent introduction of the capital framework in an AE central bank also points towards the merits of ex ante capitalization, even though the SPV route provides it with one of the strongest RTMs (whereby certain significant central banks’ risks do not enter into the central bank’s books).

(v) Treatment of revaluation balances: The cross-country survey suggests that while a few central banks do not recognize valuation gains on their balance sheets or in the profit and loss (P&L), most central banks treat the revaluation balances either as ‘limited-use risk provisions’ or as ‘risk capital’. The spectrum of the varied approaches is outlined below.

  1. Central banks which do not have revaluation balances: Central banks which do not mark-to-market their assets/ liabilities do not have revaluation balances. The same is the case with central banks following Lower of Book or Market (LoBoM) accounting which may not have revaluation balances as they do not recognize any appreciation in the value of the concerned assets. The question of such central banks using these as risk capital/ provisions, therefore, does not arise. Such central banks are a very small minority.

  2. Central banks which treat revaluation balances as limited-use risk provisions: Central banks such as the members of the ESCB recognize revaluation balances and record them directly in their balance sheet and use them to offset valuation losses to the extent of the existing balances. Losses exceeding previously recorded unrealized gains are taken to P&L; and losses on assets cannot be offset against revaluation balances of other assets. These are also not distributable. The RBI’s framework belongs to this category.

  3. Central banks which treat valuation gains as reserves: It was observed that central banks which take their valuation gains to P&L (such as the central banks which have adopted International Financial Reporting Standards [IFRS])11 generally treat them as reserves (as most central banks do not distribute unrealized gains but some do) (Bunea et al., 2016). The issue of volatility in central banks’ income, especially those with large forex holdings, is addressed through surplus smoothening mechanisms as in the case of an AE central bank, presented in Annex V.

(vi) Credit ratings of central banks: The survey revealed that a number of central banks had been rated by CRAs in the past, with many of these ratings having been unsolicited, though in certain cases such as the SNB, the rating was obtained in view of issuance of foreign currency denominated debt. Nevertheless, it was observed that the credit ratings of central banks which were not a part of any currency union were predominantly at the same level as their respective sovereigns. Rating methodology of the various CRAs (S&P, Moody’s and Dominion Bond Rating Service [DBRS]) are given in Annex VI. In this regard, the Sovereign rating methodology of S&P was updated in December 2017 to cover both the Sovereign government and monetary authorities. (The monetary authorities were till such time addressed by a separate ‘monetary authorities rating methodology’.)

(vii) Central bank operations and Sovereign ratings: It was noted that CRAs in their assessment of sovereign ratings assign weightage to areas which generally fall within the purview of central banking operations. For instance, the S&P’s sovereign credit analysis rests on five pillars of institutional assessment, economic assessment, external assessment, fiscal assessment and monetary assessment. Of these, monetary assessment depends on exchange rate policy and monetary policy. While the criteria for exchange rate assessment is whether the country has a reserve currency and its exchange rate regime; the criteria for monetary policy assessment were the following:

  1. Monetary authority independence (strong and long-established track record of full independence with clear objectives);

  2. Availability of monetary policy tools and effectiveness;

  3. Price stability;

  4. Ability to act as a LoLR for the financial system; and,

  5. Development level of local financial system and capital markets.

Source: Sovereign Rating Methodology by S&P global ratings (Dec 18, 2017), https://www.spratings.com/documents/20184/4432051/Sovereign+Rating+Methodology/5f8c852c-108d-46d2-add1-4c20c3304725

2.4 There was a view that as none of the rating parameters covers the level of economic capital held by a central bank, rating of a central bank, based on the economic capital is a misnomer. The alternative view was that global experience, as brought out in survey of literature, showed that financial resilience of a central bank was an important facilitator for achieving quite a few of the above rating criteria. The Committee noted both views.

II. Central banks’ economic capital levels as defined under the ECF

2.5 Given that one of the main points supporting the perspective that the RBI is overcapitalized is a cross-country survey based on median as the ‘measure of central tendency’ published in the Economic Survey 2016 and 2017, the Committee considered the same.

2.6 For this purpose, central banks’ economic capital, as defined under the RBI’s ECF (i.e., capital, reserves, risk provisions and revaluation balances), were assessed for all the surveyed central banks. This number does not necessarily reflect what the central banks themselves consider their own economic capital to be.12 In this regard, the RBI has an overall fifth rank at 26.8 per cent of its balance sheet in 2018 with respect to central banking economic capital, which largely emanates from revaluation balances accumulated by rupee weakness vis-à-vis the US dollar. Incidentally, RBI’s position has moderated from 2013 when it had the second highest economic capital level. Among the EMDEs, the RBI’s position is fourth. The average and median among the surveyed countries on this metric when revised for incorporating latest information as well correction of discrepancies are 8.4 per cent and 8.0 per cent respectively. The relatively high level of economic capital in the case of all the above four EMDEs is primarily on account of their substantial revaluation balances arising from currency depreciation on their forex reserves. The relatively high economic capital thus does not necessarily represent a source of strength, but rather is the imprint of previous episodes of external stress.

2.7 The Committee also reviewed the position of the central bank’s realized equity as this is the component which is actually determined by the central bank management (revaluation balances being determined in a largely autonomous manner by market price movements). The RBI’s realized equity was observed to be 7.2 per cent of the balance sheet in 2018 as revaluation balances account for 73 per cent of RBI’s economic capital.

2.8 The Committee, however, noted that drawing definitive conclusions from such comparative analysis would be difficult for the following reasons:

  1. A central bank’s economic capital requirements will vary according to its roles and responsibilities, operating environment, reserve currency status, currency convertibility status, exchange rate regime, financial stability responsibilities, accounting framework, availability of fiscally credible RTMs, etc. The impact of these factors cannot be adjusted for in the ‘measure of central tendency’ analysis.

  2. The ‘measure of central tendency’ analysis also fails to take into consideration inter-temporal variations in balance sheet size and the consequent impact on the capital size. For instance, balance sheet expansion of AE central banks, post-GFC, has resulted in the lowering of levels of capital for these central banks. The capital size of two AE central banks was around 4 per cent and 50 per cent before the GFC which has reduced to about one per cent and 16 per cent, respectively. Similar trends can be seen in the case of many other AE central banks. Further, such analysis generally fails to take into consideration that during periods of external stress and currency depreciation, the revaluation balances of the central banks typically go up - thus, high level of revaluation balances would actually be reflective of currency weakness rather than financial resilience.

  3. Central bank equity also needs to be assessed vis-à-vis vulnerabilities on the macroeconomic and financial sector front, i.e., trade balance, current account position, gross fiscal position, forex reserves, NPAs and regulatory capital/ profitability of the banking sector, to determine the adequacy of central bank equity.

  4. Central banks with negative equity should not be used for arriving at an indicative norm for the RBI as the negative capital balances would not have been consciously targeted but would have resulted from the central banking operations as well as their public policy mandate. Similarly, even among the central banks which have positive capital levels, several of them have suffered losses, the impact of which has not been captured in the analysis of the targeted level of realized equity.

2.9 The Committee noted the varied central banking practices arising due to, inter alia, the differences in their mandates, accounting frameworks, balance sheet structures and operating environments. The Committee, thereafter, reviewed the RBI-specific environment.

3 The RBI’s Public Policy Mandate, the Impact on its Balance Sheet and its Risks

3.1 Having reviewed international practices, the Committee deliberated on the RBI’s specific environment, keeping in consideration the statutory mandate under Section 47 of the Reserve Bank of India Act, 1934 and public policy mandate of the RBI, including financial stability considerations. The functions of the RBI, its public policy mandate and their implications on the balance sheet and the attendant risks are discussed ahead. The RBI’s management of its risk, its risk provisioning under the ECF and the distribution of surplus under Section 47 of the RBI Act, 1934 are covered in Chapter 4.

I. The RBI’s functions

3.2 The RBI is a full service central bank and its varied functions are briefly outlined below:

  1. Monetary authority: Formulate, operationalize and monitor the implementation of monetary policy in order to maintain price stability while keeping in mind the objective of growth.

  2. Regulator and supervisor of the financial system: Maintain public confidence in the system, protect depositors' interest and provide cost-effective banking services to the public.

  3. Regulator and supervisor of the Payment and Settlement Systems: Regulate and oversee all the payment and settlement systems in the country.

  4. Fostering of financial stability: Effecting macro-prudential policy; acting as the LoLR; developing and strengthening the deposit insurance framework within the country.

  5. Manager of foreign exchange: Administers the Foreign Exchange Management Act, 1999 (FEMA), which aims at facilitating external trade and payment and promote orderly development and maintenance of foreign exchange market in India.

  6. Reserve management: Acts as the custodian of foreign exchange reserves and manages them to calm volatility in the forex markets and provide adequate liquidity for ‘sudden stop’ or reversals in capital flows.

  7. Issuer of currency: The RBI Act confers RBI with the sole right to issue bank notes in India. The RBI’s objective is the supply and distribution of adequate quantity of currency and ensuring the quality of banknotes in circulation by continuous supply of clean notes and timely withdrawal of soiled notes.

  8. Developmental functions: Consumer protection, financial inclusion and development of institutions.

  9. Banker to the government: Banker to the Central Government vide statutory stipulations under the RBI Act, and to the state governments through various agreements.

  10. Debt manager to central and state governments: As the debt manager of central and state governments, RBI works to minimize the long-term borrowing costs, ensure sustainability of debt, and to deepen and widen the market for Government securities.

  11. Banker to banks: Maintains banking accounts of all scheduled banks and provides an efficient means of transferring funds for banks and settling inter-bank transactions.

II. Impact of the RBI’s functions and public policy objectives on its balance sheet

A broad overview of the RBI’s balance sheet dynamics

3.3 The size and composition of the RBI’s balance sheet is determined largely by the prevailing economic conditions, the external sector, its policy objectives and policy stance. To bring these inter-linkages, the balance sheet is presented in stylized form in Table 3.1.

Table 3.1: The stylized RBI balance sheet
Liabilities Per cent Assets Per cent
Capital + Reserve Fund + risk provisions + revaluation balances + other liabilities (A) 29 Foreign Currency Assets (D) 73
Gold (E) 4
Government, bank deposits (B) 18 Domestic securities (F) 17
Notes in Circulation (C) 53 Loans, advances, other (G) 6
Total 100 Total 100

Liabilities

3.4 Being the provider of domestic liquidity, the RBI’s liabilities largely consist of reserve money (typically accounting for about 70 per cent of total liabilities) and its net non-monetary liabilities (which largely represent the RBI’s economic capital).

Assets

3.5 On the asset side, the RBI’s balance sheet comprises mainly all its NFA representing, largely, the forex and gold reserves, and its NDA comprising mainly government securities. The share of NFA has varied between 65 and 90 per cent of total assets over the last 10 years. In June 2018, the share was about 77 per cent. The size, acquisition and sale of foreign assets are independent of considerations related to the balance sheet. Increases take place when the overall BoP is in surplus, either through current account surpluses or through capital account surpluses, or both; but in our context BoP surplus mostly emanates from surplus on the capital account. The foreign exchange reserves decrease sharply in years of substantial deficit on the capital account, e.g., in 2008–09.

3.6 The NFA are held in the interest of maintaining external and domestic financial and economic stability of the country. The composition of these forex reserves is determined in consultation with the Government; the reserves are spread over a basket of currencies to incorporate benefits of diversification, and the weights of currencies and the maturity of assets reflect the RBI’s long-term risk and return preferences, while ensuring their safety and liquidity. (Even here, the risk-return preferences have to take into consideration factors such as the need to maintain a major portion of reserves in the intervention currency, etc.)

3.7 The magnitude of NDA on the RBI’s balance sheet depends on the behaviour of the NFA. While accretion to the NFA results in the reserve money growth being met by such accretion, the RBI has to inject liquidity in the economy through OMO purchases in years of low growth in NFA, thereby increasing the magnitude of NDA.

Overall trends in balance sheet size and growth

3.8 The size of the RBI’s balance sheet has been around 20 per cent of the country’s nominal GDP on a relatively stable basis, with a slight downward trend over the last decade or so. It is reasonable to assume that growth in reserve money (M0) which constitutes 70 per cent of the RBI’s balance sheet will approximate growth in nominal GDP in the foreseeable future. The RBI calibrates monetary expansion on the basis of income elasticity of broad money (M3) which used to range between 1.3 and 1.4 until 2010. This, however, has reduced to about 1 over the last five to ten years. The reduction in this elasticity is consistent with the significant reduction in financial savings of households observed over this period. However, reserve money growth may witness acceleration if financial savings start increasing again.

3.9 However, while the reserve money increases by nominal GDP growth rate, the movement in Net Non-Monetary Liabilities is predominantly on account of revaluation changes in the assets of the RBI whose growth or fall depends on the changing magnitude of the NFA, exchange rate and gold price movements, interest rate movements and other developments in international financial markets and the risk provisioning by the RBI.

Impact of the RBI’s functions on its balance sheet

Monetary policy

3.10 The primary objective of monetary policy is to maintain price stability while keeping in mind the objective of growth. With the RBI adopting the Flexible Inflation Targeting (FIT) framework since 2015, the target level of inflation is sought to be achieved by influencing the level of interest rates in the economy. The objective of monetary policy operations is to enable the smooth transmission of monetary policy impulses to the financial system by ensuring that primary liquidity is consistent with the demand in the economy, such that the resulting interest rates can enable the RBI to achieve the objective of price stability, while being cognizant of growth concerns. In assessing primary liquidity (reserve money) requirements, the RBI has to meet the demand for currency from the public and liquidity needs of banks for statutory reserves. The size and growth of the RBI’s balance sheet is thus determined primarily by liability size considerations, i.e., reserve money. The balance sheet has had an annual growth of around 9.5 per cent over the past 10 years, and about 8.6 per cent in the past five-year period 2013-14 to 2017-18. The last five years’ average was low because of demonetization carried out in 2016–17. The impact of the monetary policy operations is on the following lines:

Liquidity Adjustment Facility and Open Market Operations

  1. The effect of Liquidity Adjustment Facility (LAF) and OMO on the balance sheet depends on the purpose of the action. If OMOs are conducted to increase the reserve money, it increases the size of the RBI’s balance sheet, i.e., items (B) and (F) in Table 3.1 would increase. In case these are done for sterilization purpose (mopping up the liquidity impact of capital inflows), it contracts the balance sheet, i.e., items (B) and (F) in Table 3.1 would decrease.

  2. In the case of repo operations LAF, the balance sheet expands with items (B) and (G) in Table 3.1 increasing. However, in the case of reverse repo, the size of the balance sheet is not impacted as the inter-liability accounts adjust amongst themselves.

  3. The conduct of OMO and LAF operations also impacts the profitability and surplus of the RBI, depending upon the profit/ loss incurred and interest income earned/ foregone in the case of OMOs and the interest earned/ paid in case of repurchase collateralized operations.

  4. Interestingly, even though both OMO purchase and repo operations increase the size of the balance sheet, there is considerable difference in the impact on the RBI’s risks given that the former increases the interest rate risk on the balance sheet (due to the increased size of the domestic securities portfolio) while there are no valuation risks in the repo operations as ‘loans and advances’ are not marked to market.

  5. Similarly, if OMO operations are carried out to sterilize the increase in liquidity due to forex intervention operations in the wake of capital inflows, it changes the composition of the balance sheet by increasing the forex component. This not only increases the currency risk of the RBI, but also reduces its income as it replaces high yielding domestic securities with lower yield foreign securities. This increase in risk, of course, is offset to a limited extent as the duration on the forex portfolio is shorter than the domestic portfolio, which reduces the impact of the interest rate risk.

Statutory Liquidity Ratio (SLR) and Cash Reserve Ratio (CRR)

  1. A change in CRR will change the size of the RBI balance sheet notwithstanding the fact that the CRR is now looked upon more as an instrument of prudential regulation. While any reduction in CRR is generally associated with a reduction in the liability side of the balance sheet in the form of statutory reserves maintained by banks with the RBI, the increase in CRR may increase the size of the liability side. The corresponding changes in the asset side are mainly through changes in NDA.

  2. There is limited impact of SLR change on the RBI’s balance sheet.13

Exchange rate management

3.11 While operationally the exchange rate is determined by the market, i.e., forces of demand and supply, and the level of reserves is essentially a result of sale and purchase transactions, the level also needs to be seen in the overall context of exchange rate management. The conduct of exchange rate policy is guided by the objective of modulating undue volatility and discouraging speculative activities in the foreign exchange market, while ensuring that exchange rate movements are orderly and calibrated. In this regard, the RBI interventions are not governed by a predetermined target or band around the exchange rate. To illustrate this, we look at the BoP relationship which lists all transactions made between entities in a country and the rest of the world over a defined period of time which is reflected as:

Capital Account Flows + Current Account Flows + Changes in Official Reserves Account = 0

3.12 Out of the three components of the BoP, capital flows by nature tend to be the most dominant factor in influencing the exchange rates in the short term, as capital flows tend to be larger and more volatile than the current account flows. The impact of capital inflows on the balance sheet is along the following lines:

  1. Capital inflows can be expected to increase the balance sheet size of the RBI [items (B) and (D) in Table 3.1 will increase], unless the interventions in the forex market are perfectly sterilized [items (B) and (F) will reduce]. The composition will be altered in favour of NFA in either case.

  2. The size of the NDA is then contingent upon the magnitude and direction of forex flows. During capital flight, NDA would accumulate as the central bank has to infuse liquidity. On the contrary, inflows would imply reduction of NDA to sterilize the liquidity impact of such inflows.

  3. If there are valuation gains in the forex assets due to exchange rate or interest rate movements, there is a simultaneous increase in the revaluation balances on the liability side and forex assets on the asset side resulting in an increase in the balance sheet by the same amount.

  4. Large capital inflows will lead to increase in the NFA in the balance sheet, thereby increasing currency risk. Credit risk also increases with the size of the reserves as central banks will be pressed to bring about an appropriate level of return even at the cost of taking more risk. If the central bank decides not to compromise in its counterparty standards, concentration risks could arise on the portfolio.

  5. Incidentally, the risks to RBI arising out of intervention/ sterilization operations is captured under the ECF using scenario analysis as brought out in Annex VIII. The issue of whether the adoption of the FIT regime will bring about a change in scale of RBI’s forex interventions/sterilization operations is examined in Box 3.1.

Box 3.1: Scale of RBI’s foreign market interventions /sterilization operations under the Flexible Inflation Targeting regime

The primary objective of monetary policy in India is to maintain price stability while keeping in mind the objective of growth. Price stability is a necessary precondition to sustainable growth. In May 2016, the RBI Act, 1934 was amended to provide a statutory basis for the implementation of the FIT framework. It also provided for the inflation target to be set by the Government, in consultation with the RBI, once every five years. Accordingly, the Government notified a Consumer Price Index (CPI) based inflation of 4 per cent as the target for the period August 5, 2016 to March 31, 2021 with the upper tolerance limit of 6 per cent and the lower tolerance limit of 2 per cent.

2. There is a view that with the implementation of the FIT framework, the need for carrying out foreign exchange market interventions and subsequent sterilization operations may have reduced considerably. In terms of the uncovered interest-rate parity (UIP) hypothesis, currencies with higher interest rates are expected to depreciate in order to equalize returns across currencies. The theoretical reasoning could, therefore, be that with the stabilization of inflation expectations through the implementation of FIT, there will be stability in exchange rates and the need for subsequent forex market interventions/sterilization operations will be minimal. In other words, the objective of stabilizing exchange rates is subsumed within the FIT framework and does not merit separate consideration. In reality, however, this need not be the case given that empirical evidence indicates that UIP typically holds in the medium and long run. In the short run, however, UIP is unsubstantiated, i.e., currencies with higher interest rates tend to exhibit appreciation, driven by capital flows, arbitrage opportunities and carry trade. Increasing vulnerabilities associated with a progressively open capital account, global spillovers, volatility of markets and sudden stops/ starts in capital flows are unlikely to significantly reduce the need for the RBI from intervening in the forex market in the foreseeable future, given that such interventions are addressed to quell speculative activities and maintain orderly conditions in the foreign exchange market. This is especially true in light of the vulnerability of India’s twin deficits to exogeneous factors such as global crude oil prices and change in the monetary policy stance of AEs.

Issuer of currency

3.13 The banking system would have to fund cash flows as currency is a leakage from the banking system to the extent it is held by the public as a direct claim on the central bank.

  1. If cash withdrawals are accommodated by changes in bank reserves, there is no change in the size of the balance sheet (and reserve money) although a decline in excess reserves could put pressure on interest rates.

  2. If the banking system has to take recourse to the RBI either through standing facilities or repo operations, there would be a similar expansion in the balance sheet (and reserve money) without any change in bank liquidity or interest rates.

Maintenance of financial stability

3.14 The LoLR role of the RBI can potentially have a significant impact on its balance sheet size and composition. The primary risk arising from ELA operations would be on credit exposures to distressed entities. In addition to the credit losses, the ELA operations shall have an expansionary impact on the balance sheet and would be expected to increase the share of NDA in RBI’s total assets, not only on account of the increase in the RBI’s ‘loans and advances’ portfolio but also a decrease in forex reserves in dollar terms which could be expected in view of capital flight during financial stability crises. A depreciating rupee would make the reduction in forex reserves appear to be smaller in rupee terms. These scenarios have been captured under the ECF as brought out in Annex VIII.

Banking regulator and supervisor of banks, non-banking financial companies (NBFCs) and primary dealers

3.15 This function is not expected to impact the RBI’s balance sheet directly. Nevertheless, even with an effective regulatory and supervisory framework, black swan events cannot be truly eliminated giving rise to ELA risks.

Debt manager of both central and state governments

3.16 With the Fiscal Responsibility and Budget Management Act, 2003 (FRBM Act) precluding the RBI’s operations from the primary market for government securities, this function does not significantly impact the RBI’s balance sheet. Nevertheless, it does give rise to significant operational risk.

Operating the Deposit Insurance and Credit Guarantee Corporation

3.17 As per Deposit Insurance and Credit Guarantee Corporation Act, 1961 the amount outstanding advanced by the RBI to the DICGC at any one time shall not exceed ₹5 crore rupees. Therefore, in normal times, DICGC operations will not have a significant impact on RBI’s balance sheet. However, in times of crisis, significant ELA to the DICGC cannot be ruled out. Further, the DICGC being a wholly owned subsidiary, significant losses beyond its capital could be expected to be borne by the RBI.

Development role refinance to National Bank for Agriculture and Rural Development (NABARD), National Housing Bank (NHB), Small Industries Development Bank of India (SIDBI), India Infrastructure Finance Company Limited (IIFCL)

3.18 The RBI’s development role is not expected to have any significant impact on its balance sheet. The refinance support to these entities has been discontinued since a long time.

III. The RBI’s risks

3.19 Having discussed the impact of the RBI’s functions on its balance sheet and contingent liabilities, the Committee, thereafter, reviewed the risks to which the RBI is exposed to.

Currency risk

3.20 The most significant impact of public policy considerations on the RBI’s balance sheet is the size of the forex reserves maintained to manage the volatility in the exchange rate. While these reserves provide the economy with a buffer against external stress (a public good), they give rise to significant risks for the RBI. Given that these reserves represent a ‘war chest’, they have to be maintained as open, unhedged positions14 thereby exposing the RBI to currency risk15 on more than three-fourths of its balance sheet. Consequently, the RBI suffers losses when the rupee appreciates against the USD and/ or the other currencies in its forex portfolio and it gains when the rupee depreciates against them. Thus, counter-intuitively, the RBI suffers valuation losses during times when the economy is witnessing strong growth and large capital inflows which normally are associated with rupee appreciation.16 Table 3.2 brings out the large episodes of rupee appreciation in three distinct but relatively recent time periods.

Table 3.2: Historical episodes of large USD-INR appreciation
Date Period USD INR Rupee appreciation
24/07/2006 16 months* 46.93 19.51%
(16.63% within a 12 month period)
07/11/2007 39.27
       
05/03/2009 13 months 52.06 17.38%
(13.71% within a 12 month period)
09/04/2010 44.35
       
28/08/2013 9 months 68.36 17.00%
19/05/2014 58.42

3.21 Conversely, the RBI witnesses considerable accretion to its revaluation balances (i.e., Currency and Gold Revaluation Account [CGRA]) during periods of external stress (i.e., 2008, 2011 and 2013) when the trend towards depreciation is markedly strong. This is brought out in Chart 3.1.

Chart3.1

3.22 The currency risks on the balance sheet also increases if the share of forex reserves increases as a percentage of the balance sheet. Chart 3.2 shows the changing composition of the RBI’s balance sheet with the share of forex reserves peaking in the mid 2000s due to strong capital inflows. Thereafter, there has been a fall in the share of forex reserves due to interventions during 2008, 2011 and 2013. There has been a marginal rise since then.

Chart3.2

3.23 The Committee noted that given the expanding net negative International Investment Position (IIP) of India, the magnitude of foreign exchange reserves provides confidence in international financial markets. At present, the foreign exchange reserves (more than $400 billion) are significantly lower than the country’s total external liabilities ($1 trillion) and even lower than total external debt ($500 billion). This position is in contrast to that in 2008 when India’s foreign exchange reserves, at $310 billion, exceeded the then total external debt of about US$224 billion and provided a much larger coverage of total external liabilities that amounted to about $426 billion. This needs to be taken into account in assessing the external risk being faced by the country and the possibility that the RBI may be required to increase the size of its forex reserves with its concomitant implications for the balance sheet, risks and desired economic capital. This is especially important given that the RBI’s public policy objectives of maintaining external stability during a crisis would have to be pursued irrespective of the adequacy of its risk buffers. It is, therefore, imperative that the RBI maintains a forward-looking view on the adequacy of its risk buffers even during normal times. The Committee also noted that the RBI, in consultation with the Government, periodically reviews the adequacy of the country’s forex reserves. Further, a separate internal group of the RBI is looking into the question of developing a formal framework to assess the adequacy of the forex reserves.

3.24 The Committee also deliberated on the issue of whether as the central bank, the RBI has potentially an infinite capacity to prevent rupee appreciation as it can print money to purchase foreign currency. It noted that the RBI’s interventions are carried out in line with its exchange rate policy and not to prevent losses, which would go against its public policy mandate. Further, while the RBI has significant (not infinite) ability to intervene in the market, its capability to prevent its own losses during periods of rupee appreciation was not so. It was noted that sterilization operations in 2003–04 and 2009–10 following its intervention operations caused a significant decline in its gross income.

Gold Price risk

3.25 The gold reserves are seen as strategic assets and not actively managed. The gold price risk, therefore, is fully provisioned for. This risk resulted in a valuation loss of (-) ₹16,370 crore in 2012–13 due to decrease in gold price. There is no interest rate risk for this asset.

Interest rate risks

3.26 In addition to currency risks, the RBI has significant interest rate risks on both its forex as well as its domestic securities portfolio. While the RBI does actively manage the interest rate risk on its forex portfolio, this is not possible in the case of the domestic portfolio as such operations could conflict with monetary policy operations. These risks (including residual forex interest rate risks), therefore, need to be covered by RBI’s risk provisioning.

The impact of simultaneous materialization of currency and interest rate risks

3.27 It was also observed that there have been occasions when increasing yields and appreciating rupee have materialized concurrently as indicated in Chart 3.3, resulting in considerable erosion of the RBI’s risk provisioning as seen from Table 3.3. For instance, in 2006–07, 75 per cent of RBI’s revaluation balances were wiped out amounting to 1.5 per cent of the GDP. In 2016–17, RBI’s revaluation balances fell more than ₹1 trillion due to an appreciating rupee and cross-currency movements. The only reason the markets, government fiscal balance and the economy as a whole are not impacted was that the RBI had sufficient risk provisioning to absorb these risks.

Chart3.3

Table 3.3: Decline in Revaluation Balances
Year YoY % decline As % of B/S As % of GDP
2004–05 -56.8% -5.2% -1.1%
2006–07 -75.0% -6.5% -1.5%
2009–10 -35.4% -4.5% -1.1%
2014–15 -3.1% -0.6% -0.1%
2016–17 -14.7% -3.1% -0.7%
Source: RBI, Bloomberg, Reuters, and Economic Survey 2017–18.

Do valuation risks matter or are they paper risks as they are essentially book entries? Do they require risk provisioning?

3.28 The answer is relatively straightforward: valuation risks are very real and can trigger substantial losses for the central bank. Undoubtedly, there is greater flexibility in their handling, given that they can be offset against previously accumulated valuation gains (in addition to previously accumulated realized surplus) and the concerned revaluation balances can operate in the negative through the year—till the balance sheet date (as has happened for the RBI in the case of IRA-FS in 2016–17 and 2017–18). On balance sheet date, all losses, whether they are valuation losses, credit losses, operational losses or ELA losses, have to be recognized.

3.29 In this connection, it was noted that a number of central banks have negative capital today, precisely because of their valuation losses.

Credit risk

3.30 The credit risk of the RBI is generally believed to be low on account of the following reasons:

  1. It maintains its forex reserves in high quality liquid assets (HQLA) which present low credit risks and its assets are largely in sovereign or sovereign guaranteed assets with very low default probabilities.

  2. Its domestic liquidity operations are collateralized with G-secs with margins.

3.31 The RBI's forex reserves are invested in bonds/ treasury bills that represent debt obligations of highly rated sovereigns, central banks and supranational entities. Further, deposits are placed with central banks, the BIS and overseas branches of commercial banks. Nevertheless, the measurement, monitoring and management of credit risk by a central bank is important to restrict counterparty credit risk and to ensure that the overall level of portfolio credit risk is consistent with the risk appetite of the central bank. Further, it was also recognized that complete elimination of any form of risk may not be possible (or even considered desirable from the risk-return perspective) as risks can metamorphize into unexpected forms, in unanticipated areas. The same principle applies for credit risk as well. An expanding forex portfolio, a conservative investible universe and the need for maintaining reserves in high quality and liquid assets places limitations on the possibility of diversification. This has resulted in the rising concentration of risks, with the Hirschman–Herfindahl Index (HHI) of the portfolio approximated to be 47 per cent (the HHI indicates the diversification benefits are more pronounced when the HHI has a value below 20 per cent). Further, risk also emerges from the swap facilities entered into with some of the central banks as well as the off-balance transactions entered into with domestic counterparties. Risk provisioning is required for the residual credit risk from the forex portfolio.

3.32 With regard to domestic lending operations, as indicated above, there is little credit risk as RBI’s lending operations under normal conditions are collateralized with haircuts being maintained. However, significant credit risk can arise from ELA operations during periods of stress, which is captured separately under financial stability risks.

Operational risks

3.33 Substantial operational risks emanate from the conduct of various operations of the RBI, particularly those outlined below:

  1. Management of a multi-currency portfolio across multiple time zones and legal jurisdictions as around 75 per cent of the RBI’s assets are custodized abroad.

  2. Significant market operations in domestic markets.

  3. Management and operation of a significant portion of the country’s payment and settlement systems.

  4. Large currency management operations spread across the country, etc.

Monetary and financial stability risks

Monetary operations

3.34 In addition to above market risks, the RBI’s monetary policy (sterilization) operations can significantly impact its income year-on-year as was seen in 2003–04 (-) ₹8,860 crore (a fall of 38 per cent vis-à-vis the previous year); 2009–10: (-) ₹27,848 crore (a fall of 46 per cent vis-à-vis the previous year); 2016–17: (-) ₹19,052 crore (a fall of 24 per cent vis-à-vis the previous year). Incidentally, these risks materialize when the balance sheet is already under strain due to the appreciating rupee (Chart 3.4). The RBI, nevertheless, did not suffer an overall loss during these years.

Chart3.4

3.35 The MSS does form a RTM for the RBI, though fiscal pressures can limit the extent of its use. Going forward, even with the expected implementation of the Standing Deposit Facility (SDF), sterilization risks may not necessarily be reduced as interest will have to be paid on these deposits, and unlike the OMO which were effectively limited by the extent of G-sec held by the RBI, this would not be a constraint under the SDF.

Risks arising out of financial stability mandate

3.36 While the RBI has one of the widest LoLR roles among central banks under Section 18 of the RBI Act, 1934, the ECF assesses the ‘more traditional of the ELA risks’ arising from Section 17 of the said Act. There is a view that these risks need not be covered as they have never materialized in the past and a substantial portion of the country’s banking sector is in the public sector domain.

3.37 There was another view that this represented a low probability but very high impact risk for the RBI, especially as international experience has demonstrated the vast scale of these risks as well as that contagion could spread very fast even if triggered by external sources in these days of interconnected markets. Further, the experience from GFC and more recent experiences such as Russia have shown that the ownership of the banking sector becomes more public sector oriented during periods of crisis, as the government may be required to support systemically important financial institutions to prevent contagion. This would significantly constrain the fiscal space available to the Government to recapitalize the RBI were it to suffer ELA losses. The financial stability risks of the RBI are discussed extensively in Chapter 4.

The natural smoothening of central banks’ requirement for economic capital across the business cycle

3.38 Mention may also be made of a broad smoothening of the central bank’s economic capital requirements over the various stages of the business/ economic cycle. During periods of growth, the economy can be expected to receive relatively high capital inflows, thereby increasing the size of the NFA in the balance sheet and, in the face of currency appreciation, triggering valuation losses. During periods of downturn, the size of the NDA may increase which would normally suggest a reduction in the level of currency risk and, hence, the requirement for economic capital. However, the reduction in the NFA could be a result of capital outflows/ flight or the drying of the capital inflows into the country, suggesting growing systemic risk in the economy for which central banks also require economic capital in view of the enhanced financial stability risks.

3.39 The Committee, having reviewed the RBI’s public policy mandate and impact on its balance sheet and its risks, reviewed the RBI’s extant ECF in light of the same.

4 Review of the Economic Capital Framework and Staggered Surplus Distribution Policy of RBI

4.1 As highlighted earlier, the RBI has over the years developed a number of frameworks to assess its risks and the optimal level of risk provisioning. The frameworks evolved as the balance sheet expanded both in terms of size and complexity in addition to the underlying risk profile. They were also informed by developments in methodologies for identification and measurement of risk. The Committee, having deliberated on the international practices and the implications of the RBI’s public policy mandate on its balance sheet and the risks thereof, broadly reviewed the various approaches adopted in the past to assess risk provisioning to distil useful learnings for the future. The outline of this chapter is as follows:

  1. A historical perspective of risk provisioning in the RBI

  2. The extant Economic Capital Framework

  3. The Staggered Surplus Distribution Policy

  4. Developments subsequent to the introduction of the SSDP

  5. Certain concerns with regard to the extant ECF

  6. RBI’s rationale for ECF parameterization

  7. ECF–SSDP and risk provisioning

  8. Quality of RBI’s economic capital

  9. The ECF going forward

  10. The opportunity cost of RBI’s economic capital

  11. The Surplus Distribution Policy going forward

  12. Determining whether available risk provisions are in excess of required risk provisions

  13. Treatment of excess unrealized revaluation balances

  14. Treatment of excess realized risk provisions

  15. Interim dividend and aligning RBI’s financial year with the Government’s fiscal year

  16. Periodicity of review of the ECF

I. A historical perspective of risk provisioning in the RBI

4.2 The salient recommendations of the various frameworks used to determine the risk provisioning of the RBI are summarized below, with a more detailed write-up presented in Annex VII. Further, in view of the extensive deliberations which took place in the Committee as to whether revaluation balances should be treated as risk buffers under the ECF, the Committee reviewed the approach adopted under all three methodologies:

  1. Subrahmanyam Group (1997): The Group proposed building up the CF and ADF to 12 per cent of the RBI’s balance sheet size out of the realized income of the RBI by 2005. Of this, 5 per cent was earmarked to meet shocks arising out of open market operations, 5 per cent to absorb external shocks from exchange rate volatility; and the remaining 2 per cent was proposed towards a systemic risk/developmental role. With regard to the treatment of the revaluation balances as a risk provision, the Group recognized that, in effect, while the Exchange Fluctuation Reserve (EFR), the erstwhile CGRA, absorbs the fall in gold prices, appreciation of dollar against non-dollar currencies and/or appreciation of the rupee, it may not be able to absorb large exchange rate shocks. Hence, an EFR of 5 per cent was deducted to arrive at the 5 per cent requirement of the CF for external sector risks.

  2. Malegam Committee (2014): The Committee, having reviewed apparent worst-case scenarios, proposed that no further transfers be made to CF and ADF for a period of three years (2013-14 to 2015-16), as the balances therein were in excess of assessed target requirements. This methodology, like the Subrahmanyam Group, recognized the principle that treated the revaluation balances as limited-use risk buffers against specific risks. For instance, the CGRA was treated as a risk buffer for exchange rate risk and gold price risk, while the IRA was treated as a risk buffer for interest rate risk. Any revaluation balances in excess of the specific risks were ignored.

  3. ECF (2015): Given the time frame of three years set by the Malegam Committee, work on the ECF was initiated early, which is the current framework to assess risk provisioning by the RBI. Basel methodologies are used to assess the risk provisioning requirements of the RBI. The ECF has adopted a broader approach towards revaluation balances compared to the Subrahmanyam Group or the Malegam Committee. The framework treats the revaluation balances as fungible and these are mapped against valuation risks as a whole for the RBI.17

4.3 The Thorat Committee (2004) recommendations were not accepted by the RBI.

4.4 A historical perspective (1990–91 to 2017–18) of the movement in the economic capital of the RBI (Box 4.1) was also considered.

Box 4.1: RBI’s economic capital, risk provisioning and surplus distribution (1990–91 to 2017–18)

While the concept of economic capital was first introduced in mid-2014, the various components of economic capital have been on RBI’s balance sheet for long. Certain salient points emerging from the above chart are as follows:

  1. The impact of the RBI’s risks materializing in the wake of the 1990–91 crisis is clearly visible as the CF declined from 4.5 per cent in 1990–91 to 0.5 per cent in 1992–93. Consequent to this, the Subrahmanyam Committee recommended that CF plus ADF should be built up to reach 12 per cent of the balance sheet by 2005.

  2. It is seen, post-1997, that there was a marked build-up of the CF plus ADF to 11.71/11.7 per cent of the balance sheet by 2001–02/2002–03. However, the target of 12 per cent was never reached. Thereafter, the level of CF declined (in relative terms) and stabilized at around 10 per cent of the balance sheet till around 2007–08.

  3. Between 2000–01 and 2011–12, very high volatility in the level of revaluation balances is witnessed with CGRA levels ranging from 2 per cent in 2006–07 to 14 per cent in 2008–09. During this period, the Thorat Committee report recommending that CF plus ADF plus CGRA be maintained at 18 per cent of the balance sheet was rejected in 2004–05, considering CGRA as an adjustment account and not a reserve which can be reckoned to arrive at an appropriate level of internal reserves.

  4. A renewed push towards the 12 per cent benchmark is discernible during 2008–09—the year of the GFC. However, this is short-lived and there is an almost secular downtrend of CF plus ADF to 7.05 per cent by 2017–18. The few years where there were minor upticks were during the period of high market volatility of ‘Taper Tantrum’ in May–August 2013 and then again following ‘Demonetization’ in 2016–17.

  5. The Subrahmanyam Group methodology, however, failed to take into consideration the possibility of a very sharp and sustained increase in revaluation balances which occurred in 2011–12 when the CGRA reached 22 per cent of the balance sheet. This resulted in relatively higher retention for two years, i.e. 2011–12 and 2012–13, than would have happened under ECF. Nevertheless, over the entire 18 year review period, it is only these two years where it is possible to conclude, with the benefit of hindsight, that the Subrahmanyam Committee recommendations resulted in suboptimal surplus distribution levels. The Committee noted that given the volatility in the CGRA during the previous years and the enhanced risk environment at that point in time (post US-downgrade and Taper Tantrum volatility), it may not have been anticipated that the sharp rise in revaluation balances would not be reversed, as had happened in the past.

  6. The drawdown of CF plus ADF through balance sheet expansion on the basis of the recommendations of the Malegam Committee after 2013–14 (which was supplemented by the ECF analysis from 2014–15) can be discerned.

4.5 Some of the key takeaways from the analysis presented in Box 4.1 are:

  1. The revaluation balances act as the first line of defence against market risks, as can be seen clearly between 2001–02 and 2009–10. This is particularly important given that market risk comprises the largest risk on RBI’s balance sheet.

  2. The non-inclusion of revaluation balances as market risk buffers could demonstrably result in suboptimal levels of realized risk provisioning, particularly when revaluation balances are high (as was the case from 2011–12 to 2012–13).

4.6 In light of the above, the Committee reviewed the status, need and justification of the various reserves, risk provisions and risk buffers maintained by the RBI and recommended their continuance. The Committee recommended that the RBI should explicitly recognize the ADF not only as a provision for capital expenditure, but also as a risk provision in case of need, and that appropriate disclosures to that effect may be made in its annual report. With regard to revaluation balances, the Committee recommends the following:

  1. Inclusion of the revaluation balances as a part of RBI’s overall risk buffers with the recognition of its special character.

  2. Mapping market (MTM) risks against revaluation balances (which are accumulated net MTM gains).

  3. Limited one-way fungibility between revaluation balances and realized equity to continue, whereby a shortfall in revaluation balances can be met through increased realized risk provisioning but not vice-versa.

  4. In view of international practice and RBI’s specific circumstances, the extant principle of non-distribution of revaluation balances would need to be continued as a part of the ECF.

4.7 The Committee recommends the need to draw a distinction between realized equity and revaluation balances for the following reasons:

  1. Revaluation balances are highly volatile, and whose levels move autonomously depending on RBI’s discharge of its public policy objectives of maintaining price, financial and external stability, coupled with international market developments reflected in movements in the price of foreign assets, exchange rate, interest rate and gold price.

  2. Revaluation balances cannot be used to cover risks which are not valuation risks as this can, in effect, result in the distribution of unrealized revaluation gains were such ‘non-valuation risks’ to materialize. Revaluation balances can, therefore, be treated as limited purpose risk buffers to be used against market risks only.

  3. There are significant strategic and operational constraints in the monetization of the revaluation balances (Annex VIII).

4.8 In view of the distinction sought to be made between realized equity and revaluation balances, the Committee was of the view that clearer presentation of information was required in the RBI’s Annual Accounts. This is important in light of the very different estimates of RBI’s capital which has been mentioned in the public domain.18 In the RBI’s balance sheet, while Capital and Reserve Fund are explicitly shown on the balance sheet, other sources of financial resilience are grouped under ‘Other Liabilities and Provisions’ and enumerated via Schedules making it difficult to arrive at total risk provisions. The Committee, therefore, recommends a more transparent presentation of the RBI’s Annual Accounts with regard to the components of economic capital, on the lines as indicated in Table 4.1. The Committee noted that changes in the format of presentation of balance sheet would require necessary amendments to the RBI General Regulations. The information may, therefore, be presented as a Schedule to the balance sheet till such time the processes for completing change in style of balance sheet presentation are formalized.

Table 4.1: Extant / suggested presentation of liability side of RBI’s balance sheet
Existing liabilities format Proposed liabilities format
  • Capital

  • Reserve Fund

  • Other Reserves

  • Deposits

  • Other Liabilities and Provisions

  • Notes in Circulation

  • Capital

  • Reserve Fund

  • Other Reserves

  • Risk Provisions

    • Contingency Fund

    • Asset Development Fund

  • Revaluation Accounts

  • Deposits

  • Other Liabilities

  • Notes in Circulation

4.9 After incorporating the aforementioned changes, the balance sheet of the RBI as on June 30, 2018 would appear as given in Table 4.2.

Table 4.2: Extant / suggested presentation of liability side of RBI’s balance sheet as on June 30, 2018
(₹ billion)
Existing liabilities format Proposed liabilities format
Capital 0.05 Capital 0.05
Reserve Fund 65.00 Reserve Fund 65.00
Other Reserves 2.28 Other Reserves 2.28
Deposits 6,525.97 Risk Provisions  
Other Liabilities and Provisions 10,463.04    • Contingency Fund 2,321.08
Notes in Circulation 19,119.60    • Asset Development Fund 228.11
    Revaluation Accounts 7,081.88
    Deposits 6,525.97
    Other Liabilities 831.97
    Notes in Circulation 19,119.60
Total 36,175.94 Total 36,175.94

4.10 The Committee was of the view that given the inclusion of the revaluation balances in the RBI’s overall risk buffers, measures to address volatility will have to be introduced. After examining the various options, it was decided that this would be done by articulating RTLs.

II. The extant Economic Capital Framework

4.11 Following the submission of the Malegam Committee, the RBI started developing the ECF. The ECF was first considered by the RBI’s Central Board in its March 2015 (New Delhi) meeting and, thereafter, extensively discussed at the May 2015 (Goa) meeting and a number of subsequent Central Board meetings. Discussions were also held with the Government, including meetings at the Secretary level. While the ECF analysis underpinned the surplus distribution decision for the year 2014–15, after extensive discussions at the Central Board in the August 2015 (Mumbai) meeting and finalized at the October 2015 (Aizawl) meeting, the financial resilience target for the RBI was formalized as a provisioning framework which would be consistent with the Board's aspiration to achieve, in the medium to long run, an aggregate level of provisions as usually made by bankers in order to enable it to match the highest credit rating available in international capital markets and to have sufficient additional provisions to meet financial system contingencies that may arise.

4.12 In view of the Government’s request for further discussion on the framework, extensive deliberations were held with the Government on the risk methodologies adopted under the ECF and all information sought was provided. Thereafter, the framework was formally adopted in the year 2015–16 with the transfer of RBI’s surplus to the Government for the year being unanimously approved by the Central Board at its meeting held on August 2016 (Mumbai), based explicitly on the ECF methodology, using the following parameters: S-VaR 99.99 per cent CL and a CRB target of 3 per cent with a medium-to long-term target of 4 per cent of the balance sheet.

4.13 In both the years (2014–15 and 2015–­16), the ECF facilitated the almost full transfer of surplus to the Government by providing the Central Board an assurance of the RBI’s continued financial resilience at the desired levels, thereby also complying with the recommendation of the Malegam Committee that full transfer should take place for three years (2013–14 to 2015–16). The surplus distribution decision for the first year of Malegam Committee’s recommendations, i.e. 2013–14, was back-tested under the ECF and it was observed that the full transfer was also in line with ECF’s recommendations. Thus, there were no marked differences in the implied surplus transfer as per the ECF and those recommended by the Malegam Committee for the years 2013–14 to 2015–16. The ECF assessments from June 30, 2014 to June 30, 2018 are given in Table 4.3.

Table 4.3: The level of risk exposures and available risk buffers – Evolution
Risk and buffers held As of
June 30, 2014 June 30, 2015 June 30, 2016 June 30, 2017 June 30, 2018
% of B/S* % of B/S % of B/S % of B/S % of B/S
1. Market risk (@99.99% CL) 24.5 25.3 25.1 24.3 24.4
2. Credit risk 0.4 0.4 0.4 0.4 0.4
3. Operational risk 0.4 0.4 0.4 0.4 0.3
4. Contingent risks 2.0 2.0 3.0 3.0 3.0
5. Total risk [1+2+3+4] 27.3 28.1 28.9 28.1 28.1
6. Realized equity (prior to risk provisioning for the year) 9.5 8.6 7.7 7.4 6.8
7. Risk provisioning for the year 0 0 0 0.4 0.4
8. Realized equity (after risk provisioning for the year) [6+7] 9.5 8.6 7.7 7.8 7.2
9. Revaluation balances 22.1 19.5 21.3 17.9 19.6
10. Overall risk buffers [8+9] 31.6 28.1 29.0 25.7 26.8
11. Net risk buffers (vis-à-vis target) (after transfer to GoI) [10-5] (+) 4.3 0 (+) 0.1 (-) 2.4 (-) 1.3

The technical aspects of the ECF

4.14 A detailed write-up on the ECF is given in Annex IX, with the salient features outlined in Box 4.2.

Box 4.2: The ECF of the RBI

The ECF defines a risk-based economic capital benchmark for the RBI based on international practices and its public policy mandate. The objective of holding risk equity as articulated by the RBI under the ECF is to ensure the following:

  1. The RBI has sufficient financial resilience to ensure the credibility of its policy actions, domestically and internationally, by demonstrating its financial strength to deter the markets from seeking to ‘game’ the central bank’s willingness to carry out loss-making policy actions.

  2. The RBI is seen as an unimpeachable counterparty in international transactions, even in times of stress.

  3. Sufficient buffers are maintained which may be used as a financial-stability safeguard in times of need, i.e., the country’s ‘rainy day’ provision for a financial stability crisis.

The ECF has been developed as an integral part of the Enterprise Risk Management (ERM) framework being implemented by the RBI in a phased manner since 2012. As part of this framework, the RBI has articulated its risk philosophy formally (Annex X) which, inter alia, states: ‘As financial risk considerations remain subordinate to the Bank's public-policy objectives, adequate provision is sought to be built to absorb the risks that could materialize from various eventualities.’ Accordingly, the ECF assesses this risk provisioning requirement as per the provisions of Section 47 of the RBI Act, 1934.

Risks covered under the framework and their assessment

For the balance sheet risks, i.e. market, credit and operational risks, the RBI adapted the prevailing Basel methodologies as these represented possibly the most widely accepted risk assessment methodologies.

  1. Stressed Value at Risk for market risk at 99.99 per cent CL using a 10-day return, over a time horizon of one year, using parametric distribution with a decay factor of 0.995.

  2. Standardized Approach used for credit risk.

  3. Basic Indicator Approach used for operational risk.

  4. The Contingent Risks of the RBI comprise the monetary and financial stability risks which are central bank specific and assessed using scenario analysis. The scenario analyses capture risks arising out of both ELA operations and sterilization operations.

Risk exclusions

  1. The ECF, while assessing ELA risks of the RBI, does so only under Section 17 of the RBI Act, 1934 and not under the very wide ambit of Section 18. This risk is not assessed as it is virtually impossible to assess wider liquidity provisions required from the RBI in the event of a grave crisis. Nevertheless, this statutory provision can give rise to very significant risks for the RBI.

  2. The market and credit risk of off-balance sheet exposures are not covered.

It was noted that the ECF does not explicitly assess liquidity risk (one of the largest risks for commercial banks) either for its rupee assets (as RBI being the provider of rupee liquidity is not exposed to this risk), or for its forex portfolio (as the market risk time horizon of one year covers this risk).

Components of risk equity under the ECF

The various components of risk equity under the ECF are:

  1. Capital and Reserve Fund.

  2. Realized risk provisions retained as CF and ADF.

  3. Revaluation balances which include CGRA, IRA-FS, IRA-RS, FCVA.

The treatment of revaluation balances under the ECF:

(i) No haircuts are applied on these balances while assessing the economic capital.

(ii) The framework treats all the revaluation balances as fungible and these are mapped against valuation risks as a whole for the RBI. This approach has been adopted for two reasons:

  1. The market risk of the entire market portfolio (foreign assets, domestic securities and gold) is assessed in a combined manner to maximize the benefits of diversification. There is, therefore, a single risk number representing market risk which is mapped against the combined revaluation balances.

  2. Further, the complex interplay of RBI’s diversified portfolio and market prices can result in various revaluation balances moving in different directions.

(iii) The ECF does not permit the distribution of revaluation balances. Thus, revaluation balances cannot be used to cover non-valuation risks.

Requirement for realized equity

Non-valuation risks, i.e. credit risk, operational risks and contingent risks are required to be covered by realized equity. Further, any shortfall in the revaluation balances in covering market risks will need to be covered by realized equity.

Fungibility between realized equity and revaluation balances

There is only limited fungibility in this regard as market risks can also be covered by realized equity, while revaluation balances cannot be used to cover non-market risks.

III. The Staggered Surplus Distribution Policy

4.15 The surplus distribution policy is determined by Section 47 of the RBI Act, 1934, which provides the following:

Allocation of surplus profits.

After making provision for bad and doubtful debts, depreciation in assets, contributions to staff and superannuation funds and for all other matters for which provision is to be made by or under this Act or which are usually provided for by bankers, the balance of the profits shall be paid to the Central Government.

4.16 As the RBI shifted away from targeting the relatively stable level of realized risk provisions (CF and ADF), as per the Subrahmanyam Group recommendations, to a framework which targeted economic capital (containing the volatile revaluation balances), the need for a surplus smoothening mechanism was perceived. This was particularly so as a fall in revaluation balances which reduces the economic capital and consequently surplus transferable in one year can be quickly reversed, resulting in excess economic capital in the very next year (instances highlighted in blue in Chart 4.1).

Chart 4.1

4.17 Accordingly, a surplus smoothening mechanism was initially proposed with the ECF in 2015 but was not proceeded upon in view of the Government’s objections to the same. The ECF was operationalized with the ‘default’ surplus distribution policy under Section 47 of the RBI Act, 1934.

4.18 In 2016–17, however, there was a reduction in the RBI’s equity levels due to a sharp fall in the revaluation balances brought about by the strengthening of the rupee and tightening of international yields. Further, there was a reduction in the RBI’s surplus due to the large cash management and liquidity operations carried out following the demonetization of the Specified Bank Notes. In view of these adverse balance sheet developments, no surplus would have been transferable to the Government for the year under the extant surplus distribution policy associated with the ECF.

4.19 The SSDP was developed which took into account the cyclicality in the RBI’s economic capital so that a certain degree of flexibility in surplus distribution was ensured, as outlined in Table 4.4.

Table 4.4: The Staggered Surplus Distribution Policy of the RBI
Level of RBI’s Available Equity (AvE) (excluding current year’s profit) as a proportion to Target Equity Requirement (TER) Proportion of risk provisioning by the RBI
AvE > TER 10%
AvE > 70% of TER 30%*
AvE < 70% of TER but > 40% of TER 60%*
AvE < 40% of TER but > 10% of TER 90%*
AvE < 10% of TER 100% *
* The amount to be retained may be even less if a lower amount of retention is sufficient to restore the RBI’s equity to the level of the TER.
Acceleration of retention schedule:
  1. If the RBI’s risk buffers, after the retention as suggested in the table above, remain below the target levels for two consecutive years, then an additional surplus equivalent to 10 percentage points over and above the percentage required to be held back as per the above table, shall be retained as risk provisions.19

  2. Notwithstanding anything stated above, if the Central Board is of the view that it is imperative that the proportion of risk provisioning needs to be higher than that laid out in the above schedule, then it may decide to retain the appropriate level of risk provisions prior to the transfer of surplus, if any, to the Government of India.

This framework shall be reviewed after three years.

4.20 The SSDP was placed before the Central Board in its August 2017 meeting for approval, along with a proposal for 70 per cent transfer of net income for the year 2016–17 (vis-à-vis 0 per cent under the earlier policy), which was deliberated upon and, thereafter, approved by the Central Board.

IV. Developments subsequent to the introduction of the SSDP

4.21 At the Government’s request, a system of interim dividend was initiated in 2016–17 and continued in 2017–18. Further, the annual surplus distribution for the year 2017–18 amounted to ₹500 billion against the amount of ₹385.1 billion as determined by the ECF-SSDP. Further, fresh discussions on the ECF were initiated with the Government.

V. Certain concerns with regard to the extant ECF

4.22 During the initial discussions, a concern was raised with regard to the market risk component of the extant ECF. While the ECF is an advance on the previous methodologies used by the RBI and is in line with the practice followed by some other major central banks, it was ‘risk-averse’ as no central bank was seen to be using S-VaR at 99.99 CL. Further, the correlation of RBI’s major market risks, i.e., currency risk and G-sec interest rate risk was low. As per international practice, VaR at 99 per cent CL should be used to assess the RBI’s risks.

4.23 With regard to risk provisioning for RBI’s contingency risks, there was a view that the RBI, which has never experienced any losses related to financial stability in its 84-year history has estimated the maximum level of this risk to be at 6.5 per cent of the balance sheet. It was mentioned that other central banks provided extensive liquidity support 2008 onwards without setting aside capital for ELA/LoLR for financial stability risk. Accordingly, a lower provisioning for financial stability risk was seen to be appropriate.

4.24 With regard to credit risk, there was a view that the RBI uses a combination of Basel II and loss given default (LGD) methodology of international CRAs with significant variations to estimate credit risk to arrive at a requirement of 0.4 per cent.

4.25 Regarding operational risk, there was a view that the RBI used Basel II norms while arriving at a risk provision requirement of 0.3 per cent of the balance sheet even though the likelihood of operational risk materializing was negligible.

4.26 In view of the above adopted methodologies, the RBI had one of the highest economic capital even when the RBI has never suffered a single year of realized loss. Further, seigniorage is a healthy source of income for the RBI.

VI. RBI’s rationale for ECF parameterization

4.27 The ECF was developed to assess the risk provisioning requirements for the RBI’s market risk, credit risk, operational risk and contingent risk which are generally provided for by central banks (as well as commercial banks, though their contingent risks are very different).

4.28 The RBI had adopted the then prevailing Basel methodologies for market, credit and operational risks as these represent the most widely accepted risk assessment methodologies. These were adapted, where considered necessary, to meet RBI/central banking specific conditions. The contingent risks of central banks (arising from their role as the monetary authorities and LoLR) are central bank-specific risks and scenario analyses are used to assess such risks.

Adoption of S-VaR

4.29 The RBI adopted the S-VaR approach for its ECF in 2014, after extensive discussions with the BIS:

  1. The S-VaR, at that point of time, reflected the risk management standards of the period as it was introduced globally in 2009 by the BCBS in the aftermath of the GFC to strengthen the market risk framework by addressing the limitations observed in the VaR methodology during the crisis. S-VaR is, in effect, VaR calculated using historically experienced stress conditions. This was relevant as central bank capital, in particular, should cover extreme tail risks.

  2. The RBNZ, a pioneer in the area of ECF among central banks, adopted the S-VaR for its ECF (Fraser, 2013), as had the BoE (BoE, 2017, p. 126) and the BIS (BIS, 2017, p. 236).

  3. Central banks are also known to supplement their VaR calculations with stress testing/ more stringent methodologies such as ES. Adoption of the S-VaR was in line with the use of stress factors in the determination of capital requirements.

4.30 The Committee noted that both central and commercial banks draw a distinction between the models and confidence levels used for portfolio risk management and for capital determination. For instance, the RBI uses VaR 99 per cent CL for management of its forex portfolio internally, while it uses S-VaR 99.99 per cent CL for risk provisioning. The need for greater stringency with regard to capital determination purposes is well accepted.

Selection of 99.99 per cent CL

4.31 This parameterization was chosen with the objective of RBI having the financial resilience to match the highest credit rating in international markets so as to be seen as an unimpeachable counterparty in international transactions, even in the times of crises in light of the following:

  1. The country’s EMDE status.

  2. Rising vulnerabilities associated with a progressively open capital account, global spillovers, volatility of markets and capital flows.

  3. These vulnerabilities are aggravated by India’s persistent twin deficits, i.e. both domestic fiscal and external current account deficit, with a substantial trade deficit.

  4. The lack of flexibility on the external front due to the rupee not being a reserve currency.

  5. The need to ensure credibility of RBI’s policy actions by being able to bear the risks and costs on its own.

The importance of financial resilience was seen as a relevant learning from the success of the FCNR (B) swap scheme during the Taper Tantrum of 2013 (Rajan, 2016). Given that it is the RBI’s ‘creditworthiness’ which is to be conveyed to the external sector, the framework envisages suitably providing not only for RBI’s credit risk alone but for all of its risks, including market risk, which is its most significant risk.

Need for a holistic perspective of risk parameterization to determine ‘risk averseness’

4.32 Further, the entire risk parameterization of the ECF, i.e., return period, time horizon, size of data set, distribution assumptions, components of economic capital, etc., needs to be kept in mind, and focusing only on risk model and confidence level in isolation, will lead to erroneous comparisons. For instance, using a daily return period with a lower CL such as 99.9 per cent would result in higher provisioning requirements than a 10-day return with 99.99 per cent CL.20 Similarly, a number of central banks do not treat revaluation balances as economic capital, which raises their requirement for realized equity. More importantly, it was noted that the ECF-SSDP combine permitted the RBI to continue to transfer a significant portion of its surplus to the Government even when there was significant divergence from its target levels, thereby demonstrating the risk tolerance of the RBI.

4.33 With regard to risk averseness of the credit risk methodology, it will be seen that the credit risk assessments under the extant ECF may actually be under-estimates, as concentration risk and risks of the off-balance sheet exposures are not covered therein.

4.34 With regard to operational risks, estimates using the new Standardised Approach as recommended by the latest Basel guidelines also suggest risk provisioning at similar levels as assessed using the Basic Indicator Approach under the extant methodology.

VII. ECF-SSDP and risk provisioning

4.35 Section 47 of the RBI Act, 1934 does not specify the level of financial resilience, risk models or confidence levels to be used and only specifies that the RBI has to make provisions ‘usually provided for by bankers…’. Given the interplay of risk parameters as brought out above, it is important to review the trends in surplus distribution under the ECF-SSDP framework from a historical perspective, as well as in comparison with the surplus distribution with other central banks. In this regard, the Committee noted the following:

  1. The risk provisioning by RBI, as a per cent of net income, has come down from around 50 per cent earlier to 10 per cent since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP (Chart 4.2 and Table 4.5).

  2. The RBI has transferred ₹2.65 trillion (90 per cent of its net income)21 to the Government during the same period. A comparison of the surplus transferred to the government since 1996-97 is provided in Chart 4.3 and Table 4.5.

Chart4.2

Table 4.5: Surplus distribution by the RBI
Period Surplus transferred to Government
(₹ billion)
Surplus transferred to Government
(% of net income)
1997–200322 535.7 53.1
2004–08 799.3 50.2
2009–13 1,078.0 51.3
2014–18 2,651.1 90.0

Chart4.3
  1. While the RBI does not calculate seigniorage income, the surplus transferred to the Government is substantially more than the seigniorage income, given that the transfers to the Government over the last 5, 10 and 20 years (Chart 4.4) have been 90 per cent, 74 per cent and 66 per cent, respectively, which is higher than what the RBI’s seigniorage income would be, given that Issue Department’s balance sheet size has been around 51 per cent of the RBI’s balance sheet during these periods (Chart 4.5).
Chart4.5

Chart4.5
  1. International comparison: At 90 per cent surplus transfer to the Government, the ECF-SSDP compares well with most other central banks.

  2. Surplus distribution among comparable EMDEs: RBI’s surplus distribution since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP was compared with that of two other EMDE central banks which have higher economic capital levels than the RBI due to precisely the same reason, i.e. high revaluation balances due to currency depreciation. A comparison of the surplus transfers to the Government over the same period shows that the RBI has made the lowest risk provisioning among these three EMDEs (Table 4.6).

Table 4.6: Equity position and surplus distribution of Reserve Bank of India and two other EMDE central banks
Country As on Risk Equity
(% of B/S)
Net income Profit retained
(% of net income)
Surplus transfer
(% of net income)
EMDE 1 (in billion of local currency) 31 Dec ’14 16.0 6.4 53.0 47.0
31 Dec ’15 29.8 7.8 61.5 38.5
31 Dec ’16 32.1 6.5 61.4 38.6
  31 Dec’ 17 29.7 7.5 66.5 33.5
  31 Dec’ 18 28.7 7.5 66.7 33.3
Weighted Avg.       62.1 37.9
EMDE 2 (in billion of local currency) 01 Jan ’14 14.1 69.1* 25.0 75.0
01 Jan ’15 27.6 183.3 10.0 90.0**
01 Jan ’16 35.8 112.3 10.0 90.0
01 Jan ’17 29.9 43.6 10.0 90.0
  01 Jan’ 18 27.2 0.0*** 0.0 0.0
Weighted Avg.       12.5 87.5
India (₹ billion) 30 Jun ’14 31.6 526.8 0.0 100.0
30 Jun ’15 28.1 669.0 1.5 98.5
30 Jun ’16 29.0 668.8 1.5 98.5
30 Jun ’17 25.7 438.5 30.0 70.0
  30 Jun’ 18 26.8 641.9 22.1 77.9
Weighted Avg.       10.0 90.0
* After transfer of 60 billion in local currency to a deposit insurance agency of EMDE 2.
** Includes a transfer of 27.5 billion in local currency (15% of net profit) to a development bank of EMDE 2.
*** EMDE 2 central bank made a loss in 2018 and, therefore, the same is excluded from the calculation of surplus retained/ transferred.
Source: Annual reports of the concerned central banks

VIII. Quality of RBI’s economic capital

4.36 Consequent to the transfer of 90 per cent surplus since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP, the RBI’s realized risk provisions have been reduced below the levels equivalent to 1998–99, when a conscious decision was taken to augment them in light of 1990–91 BoP crisis and Subrahmanyam Committee recommendations. The risk provisions of RBI since 1990–91 are presented in Chart 4.6.

Chart4.6

4.37 The RBI’s economic capital has undergone a significant transformation over the past 20 years, with unrealized revaluation balances now accounting for almost 73 per cent of RBI’s economic capital. Chart 4.7 brings forth the aforementioned change.

Chart4.7

4.38 The Committee observed that even if the RBI’s economic capital could appear to be relatively higher, it is largely on account of the revaluation balances which are determined by exogenous factors such as market prices and the RBI’s discharge of its public policy objectives. The proportion of realized equity to balance sheet has come down through the surplus distribution – balance-sheet expansion adjustment process since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP.

IX. The ECF going forward

4.39 The Committee, thereafter, deliberated on the benchmark for articulating the financial resilience of the RBI, the appropriate risk assessment methodology, risk model and associated parameterization to be adopted for assessing the various risks of the RBI under the ECF.

Articulation of the financial resilience of the RBI

4.40 The Committee noted that the parameterization of financial resilience under the extant ECF was guided by the Central Board’s aspiration to match the highest credit rating available in international capital markets. As brought out earlier in Chapter 2, a number of central banks had been rated by CRAs in the past, with many of these ratings being unsolicited. Nevertheless, it was observed that the credit ratings of central banks which were not a part of any currency union were predominantly at the same level as their respective Sovereign. In view of the same, the Committee recommends that, going forward, the financial resilience of the RBI may be articulated by the Central Board in terms of the risk protection desired for its balance sheet.

Selection of the risk model to be used

4.41 With regard to the risk model, the discussions focussed on the S-VaR, VaR and ES approaches and the Committee noted the following:

  1. The BCBS has recommended, under Basel III for commercial banks, the shift from S-VaR to ES. (The latter continues to be assessed under stressed conditions.)

  2. A number of central banks have moved to ES methodology and, as observed in Chapter III, there is a growing consensus on the use of ES 99 per cent CL.

  3. ES is a better risk measure for tail risk and is a coherent risk measure unlike VaR and S-VaR.

  4. The RBI has been estimating ES on a parallel basis. Thus, the necessary skill sets, application software and data sets required for migrating to ES are already in place, which marks a generational jump in risk assessment methodology and is consistent with prevalent international practices.

4.42 Given that the ECF is expected to continue serving as the RBI’s framework for assessing its risk provisioning requirement over the medium term (with suitable periodic enhancements), it is important to adopt the ES at this juncture, otherwise the ECF risks falling behind the curve in the next few years. In view of the above, the Committee recommends the adoption of the Expected Shortfall methodology for assessing the RBI’s market risk provisioning in order to secure RBI’s financial resilience.

Selection of risk parameters

4.43 The Committee considered three alternate parameterizations for ES, viz. (i) ES at 99.5 per cent CL (under stressed conditions), (ii) ES at 97.5 per cent CL (under stressed conditions), and (iii) ES at 99 per cent CL (under normal conditions). The protection provided by the three different parameterizations of ES was then assessed for adequacy in terms of a 20 per cent appreciation of INR-USD and 300 bps yield jump in the G-secs. These assessments of adequacy of financial resilience were carried out without making allowances for cross-currency risk, gold price risk, yield risk in foreign securities and forward contracts valuation risks. The Committee noted the following:

  1. The ES 99.5 per cent CL left a residual revaluation balance of 3.6 per cent for covering the excluded risks. The Committee noted that this parameterization was comparable to the overall levels suggested by the Subrahmanyam Group of 19 per cent (25 per cent of foreign assets which comprise 76 per cent of the balance sheet as on June 30, 2018) and the Malegam Committee’s recommendations which would amount to a total of around 18 per cent requirement of buffers for market risk.

  2. ES 97.5 per cent CL (under stressed conditions) provided adequate protection to meet the simultaneous occurrence of the rupee appreciation and yield jumps.

  3. The ES 99 per cent CL (under normal conditions) fell short of providing adequate protection against the identified parameters.

4.44 The results of the scenario analysis are presented in Chart 4.8. The rationale for selection of these criteria is given in Box 4.3.

Chart4.8

Box 4.3: Rationale for the selection of criteria for back-testing of ES 99.5-97.5

20 per cent rupee appreciation

As brought out in Table 3.2 (page 23), there have been several historical episodes of large rupee appreciation vis-à-vis the USD, ranging from 17.0 per cent appreciation in a nine-month period to 19.5 per cent in a distinct sixteen-month period (during the latter episode, the appreciation which took place within a 12-month horizon was 16.6 per cent). In view of the historical experience, a 20 per cent appreciation has been considered as the worst-case scenario.

300 bps yield jump in Government securities

Around 200 to 250 bps of yield jumps have been observed, at least three times in the past 15 years (Table 4.7) within a 12-month horizon. A 300 bps has, therefore, been taken as the worst-case scenario. Incidentally, if the 2009 episode horizon is expanded to 16 months, a yield jump of 294 bps is evident.

Table 4.7: Large yield jumps in 10-year G-secs
Period G-sec 10 year yield Yield Jump
20-04-04 to 08-11-04 5.062 – 7.265 220 bps
05-01-09 to 21-12-09 5.172 – 7.739 257 bps
04-06-13 to 19-08-13 7.192 – 9.228 204 bps

Simultaneous occurrence of exchange rate and interest rate risk

The simultaneous occurrence of rupee appreciation and yield jumps (or the occurrence of one risk without the second risk factor negating it), though counter-intuitive, has been seen multiple times over the previous years, as brought out in the previous chapter (Chart 3.3, page 25). As seen therein, MTM losses up to 1.1–1.5 per cent of GDP have been experienced during these periods. One of the episodes is presented in Chart 4.9. During this period, a 200 bps yield jump and 17.4 per cent rupee appreciation was witnessed almost concurrently.

Chart4.9

In a forward-looking approach, given that India is one of the fastest growing economies in the world, the Government’s ongoing economic reforms programme and the operationalization of FIT, the possibility of strong rupee appreciation in the medium term cannot be ruled out.

The Committee also considered whether the central bank suffering market losses in a period when the capital flows were strong, government finances buoyant and the country is prospering was a cause of concern. In this context, the Committee noted that the RBI suffering losses beyond its capacity, precisely at a time when monetary policy conditions were challenging due to capital inflows, would not be a desirable scenario.

The Committee noted that while the stress scenarios reflected the target financial resilience for the RBI, necessary flexibility in the framework has also been built in through the risk tolerance limit.

4.45 Shri Rajiv Kumar recalled and raised the issue flagged earlier that the stress scenario of substantial yield hardening and significant rupee appreciation over one year is not highly likely and therefore, risk tolerance range may be higher or, alternatively, trigger for realised equity to meet shortfall may be lower.

4.46 The Committee deliberated on the issue and observed that the range of ES 99.5 per cent CL to 97.5 per cent CL under stressed conditions provided adequate protection against the simulated scenarios while also providing a risk tolerance range of around 19 per cent. This compared well against the 17 per cent decline in CGRA/ 15 per cent decline in revaluation balances which occurred in 2016-17 but was reversed in the subsequent year, indicating the range of cyclical movement in recent years. The Committee was of the view that the range was an appropriate one.

4.47 In recognition of the considerations based on which the Central Board had earlier articulated the need for a high level of financial resilience for the RBI, the Committee recommends a range based on ES at a target of 99.5 per cent CL under stressed conditions with a downward tolerance threshold of ES at 97.5 per cent CL under stressed conditions. This risk parameterization was seen to provide the necessary financial resilience against the RBI’s market risks while also imparting the necessary flexibility to account for the cyclical volatility in RBI’s revaluation balances.

4.48 To take into account the volatility and cyclicality in revaluation balances, the Committee recommends:

  1. The revaluation balances may be retained as risk buffers for market risk, when revaluation balances exceed ES at 99.5 per cent CL under stressed conditions. Alternate deployment or distribution of excess revaluation balances should not be considered.

  2. Even if revaluation balances were to fall short of ES at 99.5 per cent CL under stressed conditions, additional risk provisioning will be triggered only if the RTL of ES at 97.5 per cent CL under stressed conditions is breached.

4.49 The complete parameterization for market risk recommended by the Committee is:

  1. Risk methodology: Expected Shortfall

  2. Confidence level: 99.5 (target) 97.5 (downward risk tolerance)

  3. Stress variance-covariance matrix: A period of maximum stress observed in August 2013

  4. Time horizon: One year

  5. Return period: 10-day (non-overlapping)

  6. Data set: 10 years

  7. EMWA (decay) factor: 0.995

  8. Distribution: Parametric

  9. Portfolio: Market portfolio comprising foreign assets, domestic securities and gold

4.50 The Committee also recommends that RBI should put in place a framework for assessing the market risk of its off-balance sheet exposures in view of their increasing significance.

4.51 The position as per extant parameterization and ES 99.5 per cent–97.5 per cent is given in Table 4.8:

Table 4.8: Market risk as per extant/ proposed ECF (as % of B/S)
Risk 2014
S-VaR
99.99
2015
S-VaR
99.99
2016
S-VaR
99.99
2017
S-VaR
99.99
2018
S-VaR
99.99
2018
ES 99.5 – 97.5
Market risk 24.5 25.3 25.1 24.3 24.4 18.9 – 15.3

Assessing contingent risks - financial and monetary stability risks

4.52 The financial stability risks are those rarest of rare fat tail risks which, if they do occur, can potentially devastate the economy. Central banks across the world are seen as key custodians of financial stability. Notwithstanding the formal position, as micro-prudential authority (regulator and supervisor of banks) and regulator of payment systems, the responsibility of financial stability overwhelmingly falls on the central bank. In times of stress, central banks are seen to be the LoLR (as well as MMLR), roles which are seen to be quite distinct from that of the Government’s role of ‘Recapitalizer of Last Resort’.

4.53 The Committee observed that central banks map capital, reserves and risk provisions against market risks, credit risks, operational risks as well as contingency/monetary and financial stability risks. While provisioning for financial and operational risks was relatively well acknowledged by central banks, risk assessment/provisioning for contingency/monetary and financial stability risks was an area where most central banks, including the RBI, were relatively more discreet because of the associated moral hazard in spelling it out upfront. Nevertheless, the number of central banks which strengthened their capital position after the GFC (Box 2.1) is an important indicator that central banks hold protection against such tail events in the form of capital, the highest form of risk buffers. This is also explicitly brought out in BoE’s recently implemented capital framework which stipulates that the central bank’s objectives of maintaining monetary and financial stability should be backed by its own capital, unless those operations bear a level of risk beyond the tolerance approved by Governors and Court.23 Similarly, it was observed that the ECB cited the need to provide an adequate capital base in a financial system that has grown considerably as one of the considerations for its capital increase in 2010.24 In addition, in the initial years following the GFC, a number of AE central banks made specific provisions for monetary policy operations. An East Asian central bank which increased its capital by almost 50 per cent in 2012, manages its capital and reserves at an appropriate and adequate level, in pursuit of its principal objects which include, inter alia, maintaining price stability conducive to sustainable economic growth, fostering a sound and reputable financial centre, etc. Other AE and EMDE central banks also make provisions for monetary/financial stability considerations.

4.54 In India, the position of law is such that the RBI is not only the monetary authority, but also the regulator and supervisor, inter alia of commercial banks, NBFCs, payment systems and the debt manager of the Government. The Committee agreed that the RBI has one of the widest financial stability mandates deeply entrenched in the RBI’s statute and it is also bound by Section 47 of the RBI Act, 1934 to maintain the financial resources commensurate with the task. While the potentially destabilizing events have been skilfully handled through successful mergers, acquisitions and recapitalization in the past, the Committee acknowledged that the possibility of financial stability risks materializing can never be ruled out, especially in view of the lessons learnt from the GFC.

4.55 Under normal circumstances, central banks lend mainly to banks and other eligible entities against high quality collateral, such as government securities, for a short period with adequate margin so that credit risk on central bank balance sheet is negligible. Even for the RBI, in normal times, liquidity operations pose no risks as they are collateralized with G-sec with margins. However, in a crisis of significant magnitude, banks and financial entities may exhaust their high-quality collaterals and, thereafter, would have to turn to the central bank with low-quality collateral for liquidity. In such a scenario, in the interest of financial stability, the central banks have been seen to assume substantial credit risk in its provision of ELA. The history of dilution of the collateral standards is evident among AEs as early as the 1980s when an AE central bank completely did away with the collateral requirements following the 1986 forex crisis, but then had difficulty in reintroducing them after it suffered a credit loss in 1989. This was, also, amply in evidence in the recent GFC, and exemplified by the dilution of collateral standards by leading AE central bank during the crisis (Annex XI). Incidentally, the IMF had also advised the RBI to dilute the quality of collateral during the GFC.

4.56 In addition, a crisis situation often triggers a fire sale, hampering the discovery of the fair value of a security thereby jeopardizing healthy institutions and thus intensifying the crisis. In such a situation, the central bank may have to take a security onto its books at a value higher than its crisis market value thus assuming potential credit risk in its books in the interest of systemic stability. The central bank balance sheet should have necessary resilience to address such contingencies.

4.57 Another source of contingent financial stability risk arises out of Indian banks’ global operations. Globally, the Indian banks, typically rated around BBB and below given the country’s Sovereign rating, borrow money at a spread over the respective currency London Inter-bank Offer Rate (LIBOR) tenor. The spread at which Indian banks borrow is a function of global liquidity, as also sudden developments in perceived country risk which are not quickly reflected in the country ratings. For example, in the wake of the Taper Tantrum in 2013, as also during a major banking fraud episode more recently, there were instances of tightening of ‘counterparty credit lines’ and widening of spreads. Although some of the major overseas banking regulatory jurisdictions have instituted liquidity coverage ratio (LCR) to take care of a sudden liquidity crisis, with regard to a part of Indian banks’ foreign currency liabilities (Non-Resident Indian [NRI] deposits) being carried in an onshore balance sheet, maintenance of LCR in convertible currency liquid assets is not mandatory, thus exposing them to rollover risks. In times of severe stress there is the possibility that the RBI, through deployment of its foreign exchange reserves, mitigates such rollover risks in the interest of external stability. This entails carrying risks on RBI’s balance sheet and hence may require bolstering contingent capital provisions.

4.58 Further, another critical aspect in financial stability consideration is the interconnectedness between banks and non-bank financial entities. Such interconnectedness in Indian markets is enlarging rapidly, thus increasing the risk of contagion in a financial crisis. According to the June 2019 issue of Financial Stability Report, ‘The total outstanding bilateral exposures among the entities in the financial system increased from ₹ 31.4 trillion in March 2018 to ₹ 36.3 trillion in March 2019. The public-sector banks have a net receivable position vis-à-vis the non-bank financial sector’ (RBI, 2019). In the event of a stress in the non-bank financial sector, the banking sector, and particularly the public sector banks, is likely to come under stress.25 Further, both the Government and the RBI also need to be mindful that new potential sources of financial instability, from systematically important financial institutions cannot be ruled out.

4.59 The Committee discussed the possibility of the RBI making ELA losses, even when a major part of the banking sector is in the public sector. The Committee was of the view that prudence would necessitate risk provisioning under Section 47 for the following reasons:

(i) The losses could materialize from ELA support to the private sector banks.

(ii) Having a public sector dominated banking sector does not make an economy immune to bank runs. The 2002 crisis in a Latin American economy largely involved public sector banks (PSB). Further, the experience from GFC (Annex XII) has shown that the ownership of the banking sector becomes more public sector oriented during the periods of crisis.

(iii) While large public sector ownership has been seen as a positive in preventing bank runs in the past, the NPA crisis has thrown light on the challenges that arise if a sizable majority of the banking sector looks at the Government for recapitalization. Herein lies the challenge of assessing the risk provisioning requirements of the RBI. The RBI would theoretically not be exposed to ELA losses if the Government recapitalizes these banks. However, the European debt crisis has demonstrated that private sector debt crises can transform into a Sovereign debt crisis if the Government over-stretches itself in recapitalizing the distressed banks. The position could be even more severe in India for the following reasons:

  1. Given that the Indian Sovereign’s rating is at the lowest investment grade - any downgrade, due to fiscal slippages caused by recapitalization, could exacerbate the capital flight caused by the financial crisis.

  2. The rupee not being a reserve currency will greatly limit India’s capability to manage financial crises. The fact that ELA operations by the AE central banks did not result in losses for them should not draw the central banking community into any false sense of complacency about the riskiness of such actions. Had the AEs, which are ‘issuers of reserve currencies’, not followed up their ‘qualitative easing’ programmes with the very significant ‘quantitative easing’, it is possible that their ELA operations could have ended very differently.26 Thus, Jacome et al. (2011) observes that EMDEs should be cautious in adopting the policies pursued by the AEs in the aftermath of the GFC as they would be vulnerable to currency depreciations and volatility, thereby triggering a ‘twin crisis’, i.e. a financial stability crisis as well as a BoP crisis. It is in recognition of this very vulnerability that the Central Board had previously articulated the aspiration for the highest levels of financial resilience for the RBI, which is seen as the external face of the Sovereign and the primary bulwark against external crises.

4.60 Given that the Government manoeuvrability on recapitalization of commercial banks or of the RBI could be constrained during a financial stability crisis. The Committee recognized the need for the RBI to maintain its realized equity at an appropriate level to ensure that the country is not battling a financial stability crisis with a level of financial resources that is not perceived as credible by the market. The Committee, therefore, recognized that the RBI’s financial stability risk provisions need to be viewed for what they truly are, i.e. the country’s savings for a rainy day (a financial stability crisis), built up over decades and maintained with the RBI in view of its role as the LoLR. Its balance sheet, therefore, has to be demonstrably credible to discharge this function with the requisite financial strength.

4.61 With regard to size of the CRB, various scenarios can be built and analysed. The peak liquidity scenario27 analysis approach adopted under the extant ECF suggested that the buffer should be between 2 to 6.5 per cent of the RBI’s balance sheet. The Central Board had previously decided to maintain the buffer at 3 per cent with a medium-to-long term target of 4 per cent.

4.62 The Committee was informed by the ‘peak liquidity support’ estimates arrived at in the initial implementation stages of the extant ECF as well as a separate scenario analysis to assess RBI’s ELA requirements using the methodology used by the ECB for the liquidity stress testing of commercial banks under its jurisdiction (ECB, 2019). After assessment of ELA requirements using the ECB methodology, a recovery rate ranging from 60 percent to 80 percent on the poorly collateralized borrowings which banks need to resort to after exhausting their HQLA is applied to estimate the RBI’s LoLR risks. As brought out in Table 4.9, the potential losses of the RBI range from 4.6 per cent of RBI’s balance sheet to 8.2 per cent if India’s top 10 banks get into a liquidity problem. If the crisis is bigger, widening the scenario to 55 banks, the potential losses to RBI’s balance sheet could be in the range of 6.6 per cent to 11.8 per cent. If the recovery rate is assumed lower at 60 per cent, the losses could range from 9.3 per cent to 16.4 per cent for top 10 banks and from 13.1 per cent to 23.5 per cent for 55 banks.

Table 4.9: Assessment of RBI’s LoLR risks (using ECB’s liquidity stress testing methodology for commercial banks to compute ELA)
Recovery rate (%) Adverse shock scenario (%) Extreme shock scenario (%)
  All banks (55) Top 10 banks All banks (55) Top 10 banks
60 13.1 9.3 23.5 16.4
80 6.6 4.6 11.8 8.2

4.63 The Committee considered the scenario of ELA to top 10 commercial banks with an 80 per cent recovery rate which results in a risk estimate of 4.6 per cent of the balance sheet. This analysis did not take into consideration the interconnectedness in the financial sector, the risks arising out of Indian banks’ overseas operations or the risks arising from the DICGC which is a wholly-owned subsidiary of the RBI. Accordingly, there is a need to make appropriate provisions to address the financial stability risks under the ECF. In this context, the Committee considered the following additional factors:

(i) The scale and cost of banking crises between 1970 and 2011, culled from the survey Systemic Banking Crises Database (Laeven and Valencia, 2013) is presented in Table 4.10. The scale and cost of these crises are enormous, and the risk provisioning under the ECF is, at best, moderate by these standards.

Table 4.10: Cost of financial stability – International experience
Countries Peak NPLs Fiscal costs Duration Peak liquidity Liquidity support
  % of total loans Recapitalization/ asset purchase as % of GDP Years In % of deposits and foreign liabilities
All 25 6.8 4 20.1 9.6
Advanced 5 4.2 5 11.6 6
Emerging 29.5 8.3 3 22.2 10.3
Developing 35 10 2 22.6 11.7

(ii) Chart 4.10 brings out the comparison of NPA to gross loans ratio for the countries included in the cross-country assessment, wherein India is at seventh position among the countries surveyed, indicating relatively high stress in the banking sector. Further, recent developments in the NBFC sector have indicated high levels of stress in this segment as well. The Committee also observed that a number of central banks which maintained similar capital, reserves and risk provisions as the RBI had lower NPA ratios.

Chart4.10

4.64 The Committee further noted the following:

  1. These simulations were strictly restricted to the commercial banking sector and do not cover the NBFCs to which liquidity lines have been extended recently and mutual fund segments to which liquidity lines have been offered by the RBI in the past.

  2. The simulations neither cover the risks arising from the DICGC (which is wholly owned by the RBI), as its Deposit Insurance Fund may prove insufficient to meet claims during a financial crisis, nor does it take into consideration the risks which may arise if ELA operations were to be carried out in a foreign currency.

  3. This risk provisioning represents the cushion for both financial stability as well as monetary stability risks (and was not a summation of the two sets of risks) in view of the low correlation of these risks.

4.65 Shri Rajiv Kumar recalled the issue raised earlier regarding other central banks providing extensive liquidity support 2008 onwards without setting aside capital for ELA/LoLR for financial stability risk and proposed that the provision for monetary and financial stability risk may be maintained at 3 per cent.

4.66 The matter was deliberated upon and the Committee noted that the central banks were increasingly providing for financial and monetary stability risks. This was best exemplified by the BoE’s recent MoU with the Her Majesty’s Treasury wherein they provide capital for operations that lie within its monetary and financial stability objectives, including for secured loans in normal as well as severe but plausible scenarios.

4.67 The Committee was of the view that given the importance of these risk provisions, their size should be appropriate to meet a relatively adverse financial stability shock, while ensuring the same is not excessive. The Committee recommends that the size of the monetary and financial stability risk provisions should be maintained between 4.5 to 5.5 per cent of the balance sheet.

4.68 This represented a range determined by an adverse financial stability shock lasting a month, involving the top 10 banks with an 80 per cent recovery rate.

Credit risk

4.69 The Committee reviewed the credit risk methodology and recommended the adoption of Basel III norms, given that these represented latest guidelines for assessing credit risk. Given a member’s concern, the hybrid approach was not used as had been done in the extant framework. Incidentally, the hybrid approach did not increase capital requirements while imparting dynamism to the risk estimate. The assessment of credit risk using Basel III which also covers off-balance sheet exposures leads to an increase in the provisioning requirement from the extant 0.4 per cent to 0.6 per cent of the balance sheet. The Committee recommends adopting the Basel III Standardised Approach for assessing credit risk of the forex portfolio which also covers the off-balance sheet exposures.

4.70 The Committee recommends that a suitable methodology may be developed to incorporate concentration risks into the assessment of credit risk.

4.71 The High-Level Strategy Committee for the management of forex reserves may also consider monitoring this aspect on a periodic basis.

4.72 The Committee recommends that the RBI should consider developing joint credit-market risk modelling as this would help simulate the combined impact of a crisis and may lead to lower risk provisioning due to the benefits of diversification. Given that the ECB took three to four years to put in place such a framework, if the RBI were to initiate the process now, a fully tested model could be ready within the RBI by the next review of the ECF.

Operational Risk

4.73 Under the extant ECF, provisioning for operational risk is measured with the help of Basic Indicator Approach recommended under Basel II capital adequacy rules for banking institutions. As per revised Basel III norms, the new Standardised Approach for measurement of operational risk is to be adopted. Initial estimates put the estimates under the new Standardised Approach at a marginally lower level of ₹108.1 billion from the extant position of ₹111.0 billion (as on June 30, 2018) amounting to 0.3 per cent of the balance sheet. Further, as the strengthening of the risk management framework continues, translating over time into lesser number of loss events, a decrease in operational risk provisioning can be envisaged under this approach. The Committee recommends the adoption of the new Standardised Approach for measurement of operational risk.

Size of realized equity, Contingent Risk Buffer

4.74 The Committee recommends that the size of realized equity should be adequate to provide for financial and monetary stability risks, as also credit and operational risks and recommends the size of the realized equity in the form of Contingent Risk Buffer should be 6.5 per cent of the balance sheet, with a lower bound of 5.5 per cent. This represented 1.2 to 1.4 per cent of the GDP. The recommended range may need to be supplemented in case there is any shortfall in the revaluation balances for covering market risk below the RTL of ES 97.5 per cent (stress).

4.75 The RBI’s economic capital requirement under the recommended parameters vis-à-vis the extant parameters is reflected in Table 4.11 below.

Table 4.11: RBI’s economic capital requirement
Extant ECF ECF going forward
  Market risk Contingent risk buffer  
Market risk CRB Credit risk Op risk Total Financial & monetary stability risk Credit risk Op Risk Total
24.4 3–4 0.4 0.3 28.1–29.1 18.9–15.3 4.5–5.5 0.6 0.3 25.4–20.8 #
# The CRB requirement has been rounded-up from 5.4 - 6.4 per cent to 5.5 – 6.5 per cent, as the lowest estimate of RBI’s LoLR risk is 4.6 per cent (Table 4.9) and the sum of credit and operational risk is 0.9 per cent. Thus, the lower bound of the CRB is to be maintained at 5.5 per cent with an upper bound of 6.5 per cent

X. The opportunity cost of RBI’s capital

4.76 The Committee felt that it may not be appropriate to assess the return on a central bank’s assets in pecuniary terms since the assets held by a central bank are the consequence of its variegated policy mandates, and not for pecuniary objectives. As brought out in Chapter 3, NFA are held in the interest of maintaining external and domestic financial and economic stability of the country and are independent of considerations related to the balance sheet. Similarly, NDA are also held as a consequence of the RBI’s policy mandates, inter alia, for monetary policy and liquidity management purposes, and are again independent of considerations related to the balance sheet. The RBI needs an adequate stock of G-sec for monetary policy purposes, based on its inflation targeting framework due to the following reasons:

  1. In order to operate the LAF, in times when there is excess liquidity it needs a certain stock of government securities to conduct reverse repos.

  2. In times of excess systemic liquidity, as in recent times, it conducts OMO, and sells government securities on its balance sheet.

  3. In times of excess foreign exchange flows it needs to undertake sterilization operations through the sale of government securities.

4.77 Nevertheless, if the return/ cost of RBI’s capital were to be assessed, it could be done on two broad principles:

  1. The difference in the overall return on the assets held by RBI and the average debt servicing cost for the Government

  2. The opportunity cost of capital which is the return that the Government would have generated had RBI’s capital been redeployed.

4.78 The implication of the same on the fiscal cost in terms of return on assets; the impact on debt-GDP ratio and its consequent impact on the Sovereign ratings; and the positive externalities of RBI’s risk buffers was considered by the Committee.

The fiscal cost in terms of return/ costs on assets

4.79 With regard to overall return, the assets held against risks buffers could include both a portion of the NFA and the NDA, depending on the composition of the RBI’s balance sheet at any given time. On NDA, RBI receives coupon interest on the government securities it holds, which is predominantly returned to the Government in the form of surplus transfers. On NFA, the coupon returns may be lower than on NDA, but are typically augmented by valuation returns that accrue to the revaluation balances. The positive impact of NFA on the sovereign rating reduces Government’s overall borrowing costs, and hence has an indirect pecuniary benefit.

4.80 With regard to the opportunity cost of RBI’s realized equity, given that G-sec are held against it, the fiscal impact of RBI realized equity is minimal28. No significant impact on interest expenditure would be seen if RBI’s capital is used to redeem G-sec held by it as the interest on these securities is anyway transferred back to the Government as a part of surplus transfer. Further, any transfer of RBI’s capital will reduce the future dividend transfers to the Government. Even if the Government were to redeploy RBI’s capital to fund its expenditure, contrary to expectations, the beneficial impact on Government’s interest outgo would be smaller than expected, given that the RBI may have to sell G-sec through OMOs in the market for liquidity management in line with its monetary policy stance. These OMOs could result in an increase in the interest payable by the Government to the non-RBI segment which, unlike RBI, would possibly not return the higher interest income as dividend. Thus, the opportunity cost of maintaining RBI’s capital is minimal. In this regard, Archer and Moser-Böehm (2013) also mention that capital of a central bank which is invested in government securities need not be costly when viewed from the perspective of the whole public sector.

The impact on debt-GDP ratio and its consequent impact on the Sovereign ratings

4.81 There is a view that redemption of securities held by the RBI will help to reduce India’s debt-GDP ratio, which in turn may improve the country’s credit rating. Further, the transfer of ‘excess’ capital, if any, may have a marginal impact on the country’s debt-GDP ratio29, while negatively impacting other rating criteria used by the CRAs. There was a view that the debt held against central bank’s capital could crowd out the private sector borrowings. In this regard, the Committee also noted that Meyer (2000) had observed that Government debt held by the private sector is not affected by the existence or the level of the surplus/ capital held by central banks.

4.82 Incidentally, the Malegam Committee in 2014, while recommending that no transfers be made to the CF and ADF, also recommended that the balance of surplus profits payable to the Government out of the available surplus may be restricted, at least for the next three years, to the higher of:

  1. 60% of the surplus profit (being slightly higher than the average payout ratio of the last 5 years) and;

  2. ₹35,000 crore (being slightly higher than the highest transfer to the Government in the last 5 years)

And the balance of the surplus profits may be used for redemption of part of the Government of India bonds held by RBI.

This recommendation of the Malegam Committee was not implemented, and the entire net income of the RBI was transferred to the Government.

The positive externalities of RBI’s risk buffers

4.83 As mentioned earlier, the benefits of having a well-capitalized central bank for fostering ‘monetary and financial stability’ are difficult to measure during normal times, given that these are a public good. The opportunity cost of RBI’s capital is thus seen to be relatively small, even without taking into consideration the positive externalities of monetary and financial stability which these buffers facilitate.

XI. The Surplus Distribution Policy going forward

4.84 The Committee, having recommended the target level of financial resilience for the RBI, deliberated on the surplus distribution policy which could be adopted by the RBI. Given that the ToRs require the requisite level of surplus reserves created out of realized gains to be articulated and that revaluation balances tend to be volatile, cyclical and are non-distributable, the Committee recommends that the surplus distribution policy should move away from targeting total economic capital alone, to one where it has a dual set of targets:

  1. The total economic capital of the RBI.

  2. The level at which realized equity is to be maintained.

4.85 As the market risks are mapped against revaluation balances, only the shortfall in available revaluation balances (vis-à-vis the RTL) may need to be provided as risk provisioning. The Committee, therefore recommends that, in effect, the surplus distribution policy will be required to target the ‘required realized equity’ (requirement) for covering:

  1. monetary and financial stability risks

  2. credit risk

  3. operational risk

  4. a shortfall, if any, in revaluation balances vis-à-vis market risk RTL (ES 97.5 stress).

4.86 The Committee recommends that the minimum level of realized equity to be maintained should be the sum of the monetary and financial stability risks, credit risk and operational risk.

4.87 In view of the above, the Committee recommends that the RBI move away from the SSDP, towards one which compares the ‘available realized equity’ (ARE), i.e., Capital, Reserve Fund, CF and ADF, with the ‘requirement’ and proposes surplus distribution on the following lines:

  1. Entire net income be transferred to the Government, if the RBI’s ARE is equal to or greater than upper bound of the ‘requirement’.

  2. Subject to ARE lying within the range of ‘requirement’, the Central Board may consider risk provisioning in a manner so as to maintain the RBI’s ‘ARE’ within the range of ‘requirement’ till the next periodic review.

  3. If the ARE falls short of lower bound of ‘requirement’, appropriate risk provisioning should be made by the RBI to augment realized equity to the lower bound of ‘requirement’ and only the residual net income (if any) should be transferred to the Government.

  4. If any risk provisioning from net income has been made previously for market risk, the excess realized risk provisioning over the target level of market risk buffers (ES 99.5 stress), caused by an increase in revaluation balances, may be reversed.

  5. There shall be no distribution of unrealized revaluation balances.

4.88 Box 4.4 below provides an illustration of the plausible risk provisioning requirements/surplus distribution over the next five years were the balance sheet to grow along the lines discussed therein.

Box 4.4: Assessment of requirement for risk provisioning under the proposed Surplus Distribution Policy

Going forward, the surplus distribution policy may be guided by the maintenance of ARE as recommended by the Committee. Given that the desired ‘ARE’ would be required to lie within the range of ‘requirement’ of 5.5 to 6.5 per cent, the Central Board’s decision regarding the positioning of the CRB within the range would have implications on the risk provisioning and surplus transferable to the Government. The requisite risk provisioning and surplus transferable to Government for various scenarios have been provided in table 4.12 to provide guidance both to the Central Board with regard to the extent of risk provisioning required and to the Government on the surplus to be expected every year.

While the actual net income in any year would be determined by the exact magnitude and composition of assets in the balance sheet, a simulation was carried out to assess the expected levels of risk provisioning which may be required for illustrative purposes. The Committee also observed that the growth rate of RBI’s balance sheet and income can vary significantly as seen during 1990-91 to 2017-18 (Chart 4.11). However, the trend can varies over different periods of time as brought in Annex- XIII.

Chart4.11

The size of the RBI’s balance sheet is predominantly determined by the sum of Reserve Money and movement in revaluation balances besides realized equity with the ‘residual items’ (superannuation and gratuity fund, other provisions, etc.) being relatively small. The projection for RBI’s balance sheet was carried out using a two-year lag auto regressive (AR) model. The model was chosen based on its robustness which met the requisite statistical criteria (Annex-XIII). The RBI’s balance sheet showed a substantial structural transformation with the NFA to balance sheet ratio rising from 0.08 in 1990-91 to a peak of 0.92 in 2005-06 before coming down to 0.72 by 2018-19. It is seen to undergo a structural break in its composition around 2000-01 (identified using the Chow test). Consequently, the regression analysis was carried out for the 2000-01 to 2018-19 period. The range of projection of surplus retention for the baseline (BL) scenario bound by positive shock (BL + 0.5 SD and BL + 1 SD) and negative shock (BL – 0.5 SD and BL - 1 SD) to allow for possible year-to-year volatility under immediate drawdown of realized equity to 5.5 per cent and under gradual glide path from 6.5 to 5.5 per cent of balance sheet is given in Charts 4.12 - 4.13.

Chart4.12

The results are summarised in Table 4.12.

Table 4:12: Envisaged risk provisioning for meeting ‘requirement’ of realized equity under mean circumstances
Requirement for Realized Equity Average rate of risk provisioning as per cent of net income from 2018-19 to 2022-23 under smooth glide down path
5.5 per cent 8.1 per cent (14.0 per cent)*
6.5 per cent 16.6 per cent
* Represents the average risk provisioning till 2022-23 if the realized equity is immediately drawn down to 5.5 per cent in 2018-19. In the case of 6.5 per cent target, given the extant realized equity levels, there will be no significant difference between a one-time movement in 2018-19 and a glide down path.

The Committee noted that on making reasonable allowance for volatility (± 0.5 SD and ± 1 SD) in the RBI’s net income relative to its balance sheet size, average risk provisioning over the five year period of 2018-19 to 2022-23 for CRB of 5.5 and 6.5 per cent could range from 8.1 to 16.6 per cent of net income in the normal scenario with a range of 5.4 to 11.1 per cent of net income in case of a positive shock and 16.0 to 32.8 per cent of net income in case of a negative shock respectively. The Committee also noted that these were illustrative and not exhaustive scenarios.

Assumptions underlying risk provisioning requirements

  1. The balance sheet size and net income move on the lines assumed in the model which is given in Annex XIII

  2. There may be no shortfall in revaluation balances, thus not requiring any additional risk provisioning.

XII. Determining whether available risk provisions are in excess of required risk provisions

4.89 In view of the requirements for market risk buffers and realized equity, the Committee arrived at the net position of overall risk buffers in line with the ToR 2.3.

Table 4.13: Net risk provisions as per extant and proposed ECF (June 30, 2018)
  Extant ECF Proposed ECF
  Available risk buffers Required risk buffers Net position Available risk buffers Required risk buffers Net position Excess
Market risk 19.6* plus 4.8** 24.4 - 19.6 18.9
{RTL: 15.3}
(+) 0.7 VB: 0.7
Financial & monetary stability risk 1.7 3
[medium term target: 4]
(-) 1.3
[(-) 2.3]
6.3 4.5 to 5.5 (+) 0.8 to (+) 1.8 RE: 0.8 to 1.8
Credit risk 0.4 0.4 - 0.6 0.6 - -
Op risk 0.3 0.3 - 0.3 0.3 - -
Total risks/ risk buffers 26.8 28.1
[29.1]
(-) 1.3
[(-) 2.3]
26.8 20.8 to 25.4 # (+) 1.5 to (+) 2.5^ VB: 0.7+ RE: 0.7 to 1.7#
* VB: Revaluation balances ** RE: Realized equity ^ Excess is in the form of 0.7 per cent revaluation balances and 0.8 to 1.8 per cent realized equity. {}: Risk Tolerance Limit
# As the lowest estimate of RBI’s LoLR risk is 4.6 per cent (Table 4.9) and the sum of credit and operational risk is 0.9 per cent, the lower bound of the CRB is to be maintained at 5.5 per cent with an upper bound of 6.5 per cent. Consequently, the excess RE is 0.7 to 1.7 per cent.

4.90 The Committee noted that application of its recommendations to the RBI’s balance sheet for the year 2017-18 results in excess revaluation balances of 0.7 per cent of balance sheet and excess realized equity ranging from 0.7 per cent at the upper bound of CRB to 1.7 per cent of balance sheet at the lower bound of CRB.

XIII. Treatment of excess unrealized revaluation balances

4.91 The Committee was of the view that it should not concern itself with the issue of alternative deployment of excess accumulated revaluation balances as it did not fall within the Committee’s ToRs.

4.92 As a part of the cross-country survey, the Committee noted the findings of a survey conducted by Bunea et al. (2016) pertaining to the practices followed by central banks for distribution of revaluation gains. It observed that the majority of central banks (42 out of 57) do not transfer revaluation gains, and that they record unrealized revaluation changes either on the balance sheet or, when recorded in the P&L account, they are excluded from the distributable profit. The remaining nine central banks distributed only a part of their valuation gains, while for six central banks all valuation gains were transferrable.

4.93 The survey pointed out that unrealized gains are likely to be low in the case of central banks with relatively small or actively traded portfolios similar to many commercial banks which also follow IFRS rules, while adding that the portfolio of many central banks tend to be very large and inactive. The survey highlighted that distribution of unrealized gains by central banks is increasingly not being seen as good practice. It was also stated that there is significant risk that the unrealized gains will not be realized in the future due to interest rate and exchange rate volatility resulting in losses on the eventual sale or maturity of the instruments in question which could then deplete equity and therefore have an adverse impact on the financial independence of the central bank.

4.94 The Committee noted that about half of the 53 central banks surveyed by it as a part of its own cross-country survey had a negative annual result at least once over the last five years. These were largely on account of most of these central banks taking valuation gains and losses to P&L and the valuation losses exceeding their realized net income. Incidentally, were RBI to be following this accounting approach, it too would have suffered a loss, at least in 2004–05, 2006–0730, 2009–10 and 2016–17, as valuation losses would have exceeded the RBI’s surplus in those years.

4.95 The Committee recommends that ‘excess’ revaluation balances, if any, should continue to remain on the balance sheet as risk buffers for market risk, till such time that they are realized through the sale or maturity of the underlying asset.

XIV. Treatment of excess realized risk provisions

4.96 In view of ToR 2.5 which provides that the Committee was mandated to consider any other related matter including treatment of surplus reserves, created out of realized gains, if determined to be held, given that the Committee has recommended a CRB of 5.5 to 6.5 per cent of balance sheet, the excess realized equity as on June 30, 2018 was determined to be ₹262.80 billion at 6.5 per cent and ₹624.56 billion at 5.5 per cent. The excess realized equity as on June 30, 2019 will need to be determined on the basis of RBI’s finalized annual accounts for the financial year 2018-19 as well as the level of realized equity decided upon by the RBI’s Central Board.

XV. Interim dividend and aligning RBI’s financial year with Government’s fiscal year

4.97 With regard to distribution of interim dividend, the Committee recommends that the RBI accounting year (July to June) may be brought in sync with the fiscal year (April to March) from the financial year 2020-21. Historically, the July-June year would have been linked to the agricultural seasons which is not a consideration in these times. The benefits from such a transition are manifold:

  1. The RBI would be able to provide better estimates of the projected surplus transfers to the Government for the financial year for budgeting purposes;

  2. It could reduce the need for interim dividend being paid by the RBI. The payment of interim dividend may then be restricted to extraordinary circumstances.

  3. It would obviate any timing considerations that may enter into the selection of OMO/ MSS as monetary policy tools.

  4. It would also bring about better cohesiveness in monetary policy projections, reports published by the RBI, etc., many of which are using the fiscal year as the base.

XVI. Periodicity of review of the ECF

4.98 The Committee recommends that the framework may be periodically reviewed every five years. Nevertheless, if there is a significant change in the RBI’s risks and operating environment, an intermediate review may be considered.

5 Summary of Recommendations

5.1 The Committee reviewed the status, need and justification of the various reserves, risk provisions and risk buffers maintained by the RBI and recommended their continuance. The Committee recommended that the RBI should explicitly recognize the ADF not only as a provision for capital expenditure, but also as a risk provision in case of need, and that appropriate disclosures to that effect may be made in its annual report. With regard to revaluation balances, the Committee recommends the following:

  1. Inclusion of the revaluation balances as a part of RBI’s overall risk buffers with the recognition of its special character.

  2. Mapping market (MTM) risks against revaluation balances (which are accumulated net MTM gains).

  3. Limited one-way fungibility between revaluation balances and realized equity to continue, whereby a shortfall in revaluation balances can be met through increased realized risk provisioning but not vice-versa.

  4. In view of international practice and RBI’s specific circumstances, the extant principle of non-distribution of revaluation balances would need to be continued as a part of the ECF.

(Para 4.6)

5.2 The Committee recommends the need to draw a distinction between realized equity and revaluation balances for the following reasons:

  1. Revaluation balances are highly volatile, and whose levels move autonomously depending on RBI’s discharge of its public policy objectives of maintaining price, financial and external stability, coupled with international market developments reflected in movements in the price of foreign assets, exchange rate, interest rate and gold price.

  2. Revaluation balances cannot be used to cover risks which are not valuation risks as this can, in effect, result in the distribution of unrealized revaluation gains were such ‘non-valuation risks’ to materialize. Revaluation balances can therefore be treated as limited purpose risk buffers to be used against market risks only.

  3. There are significant strategic and operational constraints in the monetization of the revaluation balances (Annex VIII).

(Para 4.7)

5.3 The Committee recommends a more transparent presentation of the RBI’s Annual Accounts with regard to the components of economic capital, on the lines as indicated in Table 5.1. The Committee noted that changes in the format of presentation of balance sheet would require necessary amendments to the RBI General Regulations. The information may, therefore, be presented as a Schedule to the balance sheet till such time the processes for completing change in style of balance sheet presentation are formalized.

Table 5.1: Extant / suggested presentation of liability side of RBI’s balance sheet
Existing liabilities format Proposed liabilities format
  • Capital

  • Reserve Fund

  • Other Reserves

  • Deposits

  • Other Liabilities and Provisions

  • Notes in Circulation

  • Capital

  • Reserve Fund

  • Other Reserves

  • Risk Provisions

    • Contingency Fund

    • Asset Development Fund

  • Revaluation Accounts

  • Deposits

  • Other Liabilities

  • Notes in Circulation

(Para 4.8)

5.4 The Committee was of the view that given the inclusion of the revaluation balances in the RBI’s overall risk buffers, measures to address volatility will have to be introduced. After examining the various options, it was decided that this would be done by articulating RTLs.

(Para 4.10)

5.5 The Committee observed that even if the RBI’s economic capital could appear to be relatively higher, it is largely on account of the revaluation balances which are determined by exogenous factors such as market prices, and the RBI’s discharge of its public policy objectives. The proportion of realized equity to balance sheet has come down through the surplus distribution – balance-sheet expansion adjustment process since the adoption of Malegam Committee recommendations/ ECF as modified by SSDP.

(Para 4.38)

Articulation of the financial resilience of the RBI

5.6 The Committee recommends that, going forward, the financial resilience of the RBI may be articulated by the Central Board in terms of the risk protection desired for its balance sheet.

(Para 4.40)

Selection of the risk model to be used

5.7 The Committee recommends the adoption of the Expected Shortfall methodology for assessing the RBI’s market risk provisioning in order to secure RBI’s financial resilience.

(Para 4.42)

Selection of risk parameters

5.8 In recognition of the considerations based on which the Central Board had earlier articulated the need for a high level of financial resilience for the RBI, the Committee recommends a range based on ES at a target of 99.5 per cent CL under stressed conditions with a downward tolerance threshold of ES at 97.5 per cent CL under stressed conditions. This risk parameterization was seen to provide the necessary financial resilience against the RBI’s market risks while also imparting the necessary flexibility to account for the cyclical volatility in RBI’s revaluation balances.

(Para 4.47)

5.9 To take into account the volatility and cyclicality in revaluation balances, the Committee recommends:

  1. The revaluation balances may be retained as risk buffers for market risk, when revaluation balances exceed ES at 99.5 per cent CL under stressed conditions. Alternate deployment or distribution of excess revaluation balances should not be considered.

  2. Even if revaluation balances were to fall short of ES at 99.5 per cent CL under stressed conditions, additional risk provisioning will be triggered only if the RTL of ES at 97.5 per cent CL under stressed conditions is breached.

(Para 4.48)

5.10 The complete parameterization for market risk recommended by the Committee is:

  1. Risk methodology: Expected Shortfall

  2. Confidence level: 99.5 (target) 97.5 (downward risk tolerance)

  3. Stress variance-covariance matrix: A period of maximum stress observed in August 2013

  4. Time horizon: One year

  5. Return period: 10-day (non-overlapping)

  6. Data set: 10 years

  7. EMWA (decay) factor: 0.995

  8. Distribution: Parametric

  9. Portfolio: Market portfolio comprising foreign assets, domestic securities and gold

(Para 4.49)

5.11 The Committee also recommends that RBI should put in place a framework for assessing the market risk of its off-balance sheet exposures in view of their increasing significance.

(Para 4.50)

Assessing financial stability risks

5.12 The Committee recognized that the RBI’s financial stability risk provisions need to be viewed for what they truly are, i.e. the country’s savings for a rainy day (a financial stability crisis), built up over decades and maintained with the RBI in view of its role as the LoLR. Its balance sheet, therefore, has to be demonstrably credible to discharge this function with the requisite financial strength.

(Para 4.60)

5.13 The Committee recommends that the size of the monetary and financial stability risk provisions should be maintained between 4.5 to 5.5 per cent of the balance sheet.

(Para 4.67)

5.14 This represented a range determined by an adverse financial stability shock lasting a month, involving the top 10 banks with an 80 per cent recovery rate.

(Para 4.68)

Credit risk

5.15 The Committee recommends adopting the Basel III Standardised Approach for assessing credit risk of the forex portfolio which also covers the off-balance sheet exposures.

(Para 4.69)

5.16 The Committee recommends that a suitable methodology may be developed to incorporate concentration risks into the assessment of credit risk.

(Para 4.70)

5.17 The High-Level Strategy Committee for the management of forex reserves may also consider monitoring this aspect on a periodic basis.

(Para 4.71)

5.18 The Committee recommends that the RBI should consider developing joint credit-market risk modelling as this would help simulate the combined impact of a crisis and may lead to lower risk provisioning due to the benefits of diversification.

(Para 4.72)

Operational Risk

5.19 The Committee recommends the adoption of the new Standardised Approach for measurement of operational risk.

(Para 4.73)

Size of realized equity, Contingent Risk Buffer

5.20 The Committee recommends that the size of realized equity should be adequate to provide for financial and monetary stability risks, as also credit and operational risks and recommends the size of the realized equity in the form of Contingent Risk Buffer should be 6.5 per cent of the balance sheet, with a lower bound of 5.5 per cent. This represented 1.2 to 1.4 per cent of the GDP. The recommended range may need to be supplemented in case there is any shortfall in the revaluation balances for covering market risk below the RTL of ES 97.5 per cent (stress).

(Para 4.74)

RBI’s economic capital requirement under the recommended parameters vis-à-vis the extant parameters

5.21 The RBI’s economic capital requirement under the recommended parameters vis-à-vis the extant parameters is reflected in Table 5.2 below.

Table 5.2: RBI’s economic capital requirement
Extant ECF ECF going forward
  Market risk Contingent risk buffer Total
Market risk CRB Credit risk Op risk Total Financial and monetary stability risk Credit risk Op Risk
24.4 3–4 0.4 0.3 28.1–29.1 18.9–15.3 4.5–5.5 0.6 0.3 25.4–20.8 #
# The CRB requirement has been rounded-up from 5.4 - 6.4 per cent to 5.5 – 6.5 per cent, as the lowest estimate of RBI’s LoLR risk is 4.6 per cent (Table 4.9) and the sum of credit and operational risk is 0.9 per cent. Thus, the lower bound of the CRB is to be maintained at 5.5 per cent with an upper bound of 6.5 per cent.

(Para 4.75)

The Surplus Distribution Policy going forward

5.22 The Committee recommends that the surplus distribution policy should move away from targeting total economic capital alone, to one where it has a dual set of targets:

  1. The total economic capital of the RBI.

  2. The level at which realized equity is to be maintained.

(Para 4.84)

5.23 The Committee, therefore recommends that, in effect, the surplus distribution policy will be required to target the ‘required realized equity’ (requirement) for covering:

  1. monetary and financial stability risks

  2. credit risk

  3. operational risk

  4. a shortfall, if any, in revaluation balances vis-à-vis market risk RTL (ES 97.5 stress).

(Para 4.85)

5.24 The Committee recommends that the minimum level of realized equity to be maintained should be the sum of the monetary and financial stability risks, credit risk and operational risk.

(Para 4.86)

5.25 In view of the above, the Committee recommends that the RBI move away from the SSDP, towards one which compares the ‘available realized equity’ (ARE), i.e., Capital, Reserve Fund, CF and ADF, with the ‘requirement’ and proposes surplus distribution on the following lines:

  1. Entire net income be transferred to the Government, if the RBI’s ARE is equal to or greater than upper bound of the ‘requirement’.

  2. Subject to ARE lying within the range of ‘requirement’, the Central Board may consider risk provisioning in a manner so as to maintain the RBI’s ‘ARE’ within the range of ‘requirement’ till the next periodic review.

  3. If the ARE falls short of lower bound of ‘requirement’, appropriate risk provisioning should be made by the RBI to augment realized equity to the lower bound of ‘requirement’ and only the residual net income (if any) should be transferred to the Government.

  4. If any risk provisioning from net income has been made previously for market risk, the excess realized risk provisioning over the target level of market risk buffers (ES 99.5 stress), caused by an increase in revaluation balances, may be reversed.

  5. There shall be no distribution of unrealized revaluation balances.

(Para 4.87)

Determining whether available risk provisions are in excess of required risk provisions

5.26 In view of the requirements for market risk buffers and realized equity, the Committee arrived at the net position of overall risk buffers in line with the ToR 2.3.

Table 5.3: Risk provisions as per extant and proposed ECF (June 30, 2018)
  Extant ECF Proposed ECF
Available risk buffers Required risk buffers Net position Available risk buffers Required risk buffers Net position Excess
Market risk 19.6* plus 4.8** 24.4 - 19.6 18.9
{RTL: 15.3}
(+) 0.7 VB: 0.7
Financial & monetary stability risk 1.7 3
[medium term target: 4]
(-) 1.3
[(-) 2.3]
6.3 4.5 to 5.5 (+) 0.8 to (+) 1.8 RE: 0.8 to 1.8
Credit risk 0.4 0.4 - 0.6 0.6 - -
Op risk 0.3 0.3 - 0.3 0.3 - -
Total risks/ risk buffers 26.8 28.1
[29.1]
(-) 1.3
[(-) 2.3]
26.8 20.8 to 25.4 # (+) 1.5 to (+) 2.5^ VB: 0.7+ RE: 0.7 to 1.7#
* VB: Revaluation balances ** RE: Realized equity ^ Excess is in the form of 0.7 per cent revaluation balances and 0.8 to 1.8 per cent realized equity. {}: Risk Tolerance Limit
# As the lowest estimate of RBI’s LoLR risk is 4.6 per cent (Table 4.9) and the sum of credit and operational risk is 0.9 per cent, the lower bound of the CRB is to be maintained at 5.5 per cent with an upper bound of 6.5 per cent. Consequently, the excess RE is 0.7 to 1.7 per cent.

(Para 4.89)

5.27 The Committee noted that application of its recommendations to the RBI’s balance sheet for the year 2017-18 results in excess revaluation balances of 0.7 per cent of balance sheet and excess realized equity ranging from 0.7 per cent at the upper bound of CRB to 1.7 per cent of balance sheet at the lower bound of CRB.

(Para 4.90)

Treatment of excess unrealized revaluation balances

5.28 The Committee recommends that ‘excess’ revaluation balances, if any, should continue to remain on the balance sheet as risk buffers for market risk, till such time that they are realized through the sale or maturity of the underlying asset.

(Para 4.95)

Treatment of excess realized risk provisions

5.29 Given that the Committee has recommended a CRB of 5.5 to 6.5 per cent of balance sheet, the excess realized equity as on June 30, 2018 was determined to be ₹262.80 billion at 6.5 per cent and ₹624.56 billion at 5.5 per cent. The excess realized equity as on June 30, 2019 will need to be determined on the basis of RBI’s finalized annual accounts for the financial year 2018-19 as well as the level of realized equity decided upon by the RBI’s Central Board.

(Para 4.96)

Interim dividend and aligning RBI’s financial year with Government’s fiscal year

5.30 With regard to distribution of interim dividend, the Committee recommends that the RBI accounting year (July to June) may be brought in sync with the fiscal year (April to March) from the financial year 2020-21. Historically, the July-June year would have been linked to the agricultural seasons which is not a consideration in these times. The benefits from such a transition are manifold:

  1. The RBI would be able to provide better estimates of the projected surplus transfers to the Government for the financial year for budgeting purposes;

  2. It could reduce the need for interim dividend being paid by the RBI. The payment of interim dividend may then be restricted to extraordinary circumstances.

  3. It would obviate any timing considerations that may enter into the selection of OMO/ MSS as monetary policy tools.

  4. It would also bring about better cohesiveness in monetary policy projections, reports published by the RBI, etc., many of which are using the fiscal year as the base.

(Para 4.97)

Periodicity of review of the ECF

5.31 The Committee recommends that the framework may be periodically reviewed every five years. Nevertheless, if there is a significant change in the RBI’s risks and operating environment, an intermediate review may be considered.

(Para 4.98)


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8. Bank Indonesia Annual Report various issues.

9. Bank Negara Malaysia Annual Report various issues.

10. Bank of England (2017). Annual Report and Accounts – 2017; p 126. Retrieved from https://www.bankofengland.co.uk/-/media/boe/files/annual-report/2017/boe-2017.pdf?la=en&hash=32D14F11EF6AA4E3D708C168819F112FAC1D3681

11. Bank of England (2018). ‘Financial relationship between HM Treasury and the Bank of England: Memorandum of Understanding.’ Retrieved from https://www.bankofengland.co.uk/-/media/boe/files/memoranda-of-understanding/financial-relationship-between-hmt-and-the-boe-memorandum-of-understanding.pdf

12. Bank of Korea Annual Report various issues.

13. Bank of Russia Annual Report various issues.

14. Basel Committee on Banking Supervision (2016). ‘Minimum capital requirements for market risk’, published January 14, 2016. Retrieved from https://www.bis.org/bcbs/publ/d352.pdf

15. Benecká, S., Holub, T., Kadlčáková, N.L., & Kubicová I. (2012). “Does Central Bank Financial Strength Matter for Inflation? An Empirical Analysis”, Czech National Bank Working Papers, series 3, Czech National Bank.

16. Bindseil, Ulrich, Manzanares, Andres & Weller, Benedict (2004). ‘The Role of Central Bank Capital Revisited’, ECB Working Paper No. 392, September. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp392.pdf?e7bba27cdd7b653529fc35400ff0a3c0.

17. Buiter, Willem H. (2008). ‘Can Central Banks Go Broke?’, CEPR Discussion Papers No. 6827, London, Centre for Economic Policy Research. Retrieved from https://cepr.org/active/publications/discussion_papers/dp.php?dpno=6827 (last accessed 4 June 2019).

18. Bunea, Daniela, Karakitsos, Polychronis, Merriman, Niall, & Studener, Werner (2016). ‘Profit Distribution and Loss Coverage Rules for Central Banks’, ECB Occasional Paper Series, No. 169, April. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpops/ecbop169.en.pdf?f7073b79c2f6f62c79918dc24088dd00 (last accessed 4 June 2019).

19. Crotty James (2009). ‘Structural Causes of the Global Financial Crisis: A Critical Assessment of the ‘New Financial Architecture’’, Cambridge Journal of Economics 2009, 33, 563–580

20. Dziobek Claudia, Dalton John (2005). ‘Central bank losses and experiences in selected countries’, IMF Working Paper No. 05/72. Retrieved from https://www.imf.org/external/pubs/ft/wp/2005/wp0572.pdf

21. Ernhagen, Tomas, Vesterlund, Magnus, & Viotti, Staffan (2002). ‘How Much Equity Does a Central Bank Need?’, Sveriges Riksbank Economic Review. Retrieved from http://archive.riksbank.se/Upload/Dokument_riksbank/Kat_publicerat/Artiklar_PV/er02_2_artikel1.pdf.

22. European Central Bank Annual Report various issues.

23. European Central Bank (2019). ‘Sensitivity Analysis of Liquidity Risk – Stress Test’.

24. Frait, J. (2005). ‘Exchange Rate Appreciation and Negative Central Bank Capital: Is There a Problem?’, Speech presented at Expert Forum: Central Bank Finances and Impact on Independence, Centre for Central Banking Studies, Bank of England, London.

25. Fraser (2013). ‘The Reserve Bank's Capital Adequacy Framework’, The Reserve Bank of New Zealand Bulletin, September 2013, Volume 76, No. 3.

26. Friedman, M., and Schwartz, A.J. (1963). A Monetary History of the United States, 1867–1960, A Study by The National Bureau of Economic Research, Princeton: Princeton University Press.

27. Gopalakrishna, G. (2013). Inaugural speech at ‘Federal Reserve System Market Risk Analysis Seminar’ at New Delhi, January 21, 2013. Retrieved from /en/web/rbi/-/speeches-interview/reserve-bank-of-india-federal-reserve-system-market-risk-analysis-seminar-771

28. Hall, R.E., & Reis, R. (2015). ‘Maintaining Central-Bank Financial Stability under New-Style Central Banking’, NBER Working Paper No. 21173. Retrieved from https://www.nber.org/papers/w21173.

29. Hastowo,M. Agung, & Indarto, Tonny (2016). ‘Fundamental Principles of Central Bank Financial Reporting: A Preliminary Study in SEACEN Economies’. Retrived from https://www.seacen.org/publication-research.php?pid=702004-100391

30. Jacome, H., Luis, I., Sedik, Tahsin, Saadi & Townsend Simon (2011). ‘Can Emerging Market Central Banks Bail Out Banks? A Cautionary Tale From Latin America’, IMF Working Paper No. 11/258. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Can-Emerging-Market-Central-Banks-Bail-Out-Banks-AL4848-Cautionary-Tale-From-Latin-America-25334

31. Kathleen McDill (2004). ‘Resolution Costs and the Business Cycle’, FDIC Working Paper 2004-01, March 2004

32. Klüh, U. & Stella, P. (2008). ‘Central Bank Financial Strength and Policy Performance: An Econometric Evaluation’, IMF Working Papers No. 08/17 International Monetary Fund, Washington, DC.

33. Krugman, Paul R. (1998). ‘It's Baaack: Japan's Slump and the Return of the Liquidity Trap’, Brooking Papers on Economic Activity, 29(2), 137–87.

34. Lahiri, Amartya, Bandyopadhyay, Sujan, Devnani, Rishab & Ghosh, Sudipta (2019). ‘Central Bank Equity: Facts and Analytics’, CAFRAL and University of British Columbia.

35. Luc Laeven, Fabián Valencia (2013). ‘Systemic Banking Crises Database’, IMF Economic Review, June 2013, Volume 61, Issue 2, pp 225-270.

36. Meyer, Laurence H. (2000). ‘Payment of interest on reserves and Fed surplus’, testimony dated May 3, 2000 before the Committee on Banking and Financial Services, U.S. House of Representatives. (https://www.federalreserve.gov/boarddocs/testimony/2000/20000503.htm)

37. Perera, A., Ralston, D., & Wickramanayake, J. (2013). ‘Central Bank Financial Strength and Inflation: Is There a Robust Link?’, Journal of Financial Stability, 9, 399–414.

38. Rajan, Raghuram G. (2016). ‘The Independence of the Central Bank’, Speech dated 3 September 2016 at St. Stephen’s College, Delhi. Retrieved from /en/web/rbi/-/speeches-interview/the-independence-of-the-central-bank-1021

39. Reserve Bank of India (2019). ‘Financial Stability Report June 2019’. Retrieved from /documents/87730/39711208/FSRJUNE2019e5ecddad7e514756afef1e71cb2ada2b.pdf

40. Reserve Bank of Australia Annual Report various issues.

41. Reserve Bank of New Zealand Annual Report various issues.

42. Restrepo Jorge, Salomó Luis & Valdés Rodrigo (2008). ‘Macroeconomics, Monetary Policy and the Central Bank´s Net Worth’, Working Papers Central Bank of Chile 497, Central Bank of Chile.

43. Sims, C.A. (2013). ‘Paper Money’, American Economic Review, 103(2), 563–84.

44. South African Reserve Bank Annual Report various issues.

45. Stella, Peter (1997). ‘Do Central Banks Need Capital?’, IMF Working Paper No. 97/83. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2016/12/30/ Do-Central-Banks-Need-Capital-2260.

46. —(2005). ‘Central Bank Financial Strength, Transparency, and Policy Credibility’, IMF Staff Papers, vol. 52, International Monetary Fund, Washington, DC, pp. 335–65.

47. Stella, Peter & Lönnberg, Åke (2008). ‘Issues in Central Bank Finance and Independence’, IMF Working Paper No. 08/37. Retrieved from https://www.imf.org/external/pubs/ft/wp/2008/wp0837.pdf.

48. Sveriges Riksbank Annual Report various issues.

49. Swiss National Bank Annual Report various issues.

50. The Riksbank’s Financial Independence (2007). Commission of Inquiry report (SOU2007: 51)

51. Thomas F. Cargill (2005): “Is the Bank of Japan's Financial Structure an Obstacle to Policy?”; IMF Staff Papers Vol. 52, Number 2, 311–34.

In addition to the above given central banks, annual reports of many other central banks were examined.


Annex III

Economic Capital Framework of other central banks

1. Bank of England

The objective of the BoE capital framework is to provide a robust and transparent process to ensure that the BoE has the financial resources needed to undertake the financial operations necessary to deliver its objectives even under severe but plausible scenarios. The purpose of BoE capital is that operations that lie within the BoE’s objectives of maintaining monetary and financial stability should be backed by its own capital, unless those operations bear a level of risk beyond the tolerance approved by Governors and Court. The following types of operations should be backed by capital:

  • Secured lending in line with the BoE’s published frameworks, including against eligible collateral;

  • Asset purchase operations to support conventional monetary policy implementation, the BoE’s official customer business or the funding of the BoE.

The actual level of the BoE’s loss-absorbing capital at any point in time should allow it to continue to undertake the operations, both in normal market and liquidity conditions and under a set of severe but plausible scenarios, without falling below the capital floor. These scenarios are approved by Governors and Court. The financial backing for other operations, including those covered under the ‘Memorandum of Understanding on resolution planning and financial crisis management’, unconventional monetary policy asset purchases and MMLR operations should be assessed on a case-by-case basis. The parameters of the BoE’s capital framework will be formally reviewed by the BoE and the Treasury at least every five years. However, in circumstances where the risk environment faced by the BoE changes fundamentally, an intermediate review may be warranted.

Capital requirements will be set considering both the BoE’s current balance sheet and its contingent commitments to provide liquidity insurance to the financial system. Other factors, such as potential future changes to BoE facilities that the BoE indicates may be necessary to enable it to achieve its objectives, will also be considered. The parameters of the capital framework include a target, a floor, and a ceiling.

Target

(i) The target will be calculated using a forward-looking, scenario-based approach to assess potential losses in a set of severe but plausible events, for activities that are backed by the BoE’s capital. When the BoE’s capital is below the target, whether above or below the floor, the BoE will not make payments in lieu of dividends to the Treasury until such time as the target is reached.

Floor

(ii) The floor will be set as the level below which the credibility of the BoE’s ability to deliver its mission would be in sufficient jeopardy to warrant timely action. Should the BoE’s capital fall below that floor, it will be important to take rapid and decisive steps to restore the BoE’s capital to underpin confidence in the BoE.

Ceiling

(iii) This will be set at a level that enables the BoE’s capital to withstand substantial losses without falling below the target by the end of the five-year period. Specifically, the distance between the ceiling and the target will be no less than two-thirds of the distance between the target and the floor and no more than the distance between the target and the floor. Once the BoE’s capital is above the ceiling, no further income is retained, and 100 per cent of net profits for the financial year in which the ceiling is exceeded and for any future years that it is exceeded will be paid in lieu of dividend by the BoE to the Treasury. If the BoE’s capital is above the target, but below the ceiling, the BoE will pay 50 per cent of net profits for the financial year in which the capital target is exceeded and for any future years that it is exceeded, in lieu of dividend to the Treasury.

Source: https://www.bankofengland.co.uk/letter/2018/banks-financial-framework-june-2018

2. European Central Bank

Since 2007, the ECB has reported in its Annual Accounts the financial risks relating to all of its portfolios combined, as measured by the financial VaR at a 95 per cent CL over a one-year horizon. As on 31 December 2018 - as reported in the 2018 Annual Accounts – the subscribed and paid-up capital amounted to €10.8 billion and €7.7 billion respectively. In recent years the ECB has enhanced its risk modelling framework. Some of the changes implemented include the following:

  1. The ECB now uses the ES at a 99 per cent CL as the main measure for risk calculations, with other risk measures and confidence levels being used to provide complementary information.

  2. An ‘accounting approach’ has been devised in addition to the existing ‘financial approach’. Under the financial approach, the revaluation accounts are not considered as a buffer in the calculation of risks, whereas under the accounting approach risks are quantified after considering the revaluation accounts, in line with the applicable accounting rules. Therefore, the two approaches reflect two different ways of looking at risks: the financial approach considers their impact on the ECB’s net equity, whereas the accounting approach considers their impact on the ECB’s P&L account.

The accounting approach is deemed more appropriate in the context of the Annual Accounts as it offers a clearer picture of the risks in terms of their accounting consequences. Therefore, also seeking to align published data with the internal risk modelling and reporting approach, the ECB’s Annual Accounts will, henceforth, report the ES at a 99 per cent CL following the accounting approach, instead of the VaR at a 95 per cent CL following the financial approach.

Depending on the size of the ECB’s revaluation accounts, the financial and accounting approaches for measuring risks can result in significantly different risk estimates in terms of their size and composition. In particular, the financial approach, using the same risk measure and confidence level, results in larger risk estimates, mainly dominated by sizeable market risks associated with foreign reserve holdings. Since significant revaluation accounts exist for such exposures, the accounting approach results in lower risk figures, mainly driven by potential credit risk events.

The changeover from the financial VaR 95 per cent to the accounting ES 99 per cent in the Annual Accounts for 2017 results in a higher risk estimate in nominal terms as the increase in the risk estimate from choosing a higher confidence level (99 per cent instead of 95 per cent) and a more conservative risk measure (ES instead of VaR) more than compensates for the reduction in the risk estimate brought about by considering the revaluation accounts as a buffer.

Source: ECB Annual Report 2017

3. Reserve Bank of Australia

The RBRF is the RBA’s general reserve and is the main component of the RBA’s capital. This reserve is funded from transfers from earnings available for distribution. Its purpose is to provide the capacity to absorb losses when it is necessary to do so.

The Reserve Bank Board has a framework to assess the target balance of the RBRF by assessing and appropriately assigning capital to exposures of different risk. The largest potential for loss from the RBA’s assets comes from market risk, comprising foreign exchange and interest rate risk. The capital assigned to each component of market risk is derived from the RBA’s historical experience of loss and stress tests of the balance sheet, which incorporate significant adverse movements in exchange rate and interest rates drawn from historical experience. Since the largest potential for loss is associated with the RBA’s unhedged holdings of foreign exchange assets, materially more capital is assigned to exchange rate risk than to interest rate risk.

While the RBA has no history of loss from credit risk, credit risk is also incorporated into the capital framework. The capital held against credit risk is currently a small sum, reflecting the quality of assets the RBA holds, the soundness of counterparties with which it deals, the fact that repurchase agreements and foreign exchange swaps are well collateralized and that the RBA follows a set of conservative policies to manage credit risk, consistent with its very low appetite for such risk. Capital, therefore, is held only against the RBA’s very small exposures to commercial banks that are not collateralized. This overall approach to credit risk is consistent with the practice of a range of major central banks.

The balance of the unrealized profits reserve stood at $5,860 million on 30 June 2018, a rise of $3,178 million from the previous year. This movement largely reflects unrealized revaluation gains associated with the depreciation of the Australian dollar. The balance of this reserve is available either to absorb future revaluation losses or to be distributed over time as the gains are realized when relevant assets are sold.

The current balance in the RBRF ($14,119 million) slightly exceeded the Reserve Bank Board's target at the end of 2017/18. Accordingly, the Board viewed the balance sheet as being very strong and members saw no need to seek further capital from 2017/18 profits. The Treasurer, after consulting the Board, therefore determined that all earnings available for distribution in 2017/18, a sum of $669 million, would be paid as a dividend to the Commonwealth.

Source: Reserve Bank of Australia Annual Report 2018

4. Reserve Bank of New Zealand

The RBNZ employs an ECF that ensures that the Bank is unlikely, within a 99.9 per cent CL, to suffer a financial loss through credit, market or operational risks that would result in negative equity.

The RBNZ uses market and credit risk models using both standard and S-VaR models, and applies them to its traded and non-traded portfolios to model the its capital requirement. An allowance for operational risk is also added. Key inputs in capital modelling include interest rate and foreign currency positions and limits, foreign and local currency investments and counterparty credit exposures, as well as the probability of loss with respect to each of these factors.

The calculation of required capital is assessed by the Bank’s Asset and Liability Committee and the Governing Committee. In making that assessment, consideration is given to whether a capital buffer needs to be retained for hypothetical events such as an extreme economic shock or foreign currency market event. No additional capital buffers were provided as at the reporting date (2017). The Board and Minister review the RBNZ’s assessment of required capital when considering its annual dividend recommendation.

To ensure that unrealized gains are not distributed, after a provision for dividend is made, Net Assets/Equity Excluding Unrealized Gains should not be less than required capital.

Source: RBNZ Annual Report 2017–18

5. Central banks with target-level reserves and risk provisions

  1. The Norges Bank requires allocations to be made from its profit to the Adjustment Fund until the Fund has reached 5 per cent of the Bank’s holdings of Norwegian securities and 40 per cent of the Bank’s net forex reserves (Page 66, Annual Report – 2017).

  2. The SNB, which till recently linked its reserve calculations to the average growth of nominal GDP over the preceding five years, now does so at twice that rate, given the heightened risks (Page 159, Annual Report – 2017).

  3. BdF, in addition to its General Fund, must also maintain a reserve which must be equal to at least 12 per cent of its gold and foreign currency position; this must also be sufficient to cover the losses that would arise from a fall in prices equivalent to the worst price fall of the past ten years (Page 113, Annual Report – 2017).

  4. In the case of the US FED System, each member commercial bank of the 12 Federal Reserve Banks (FRB), each of which is a separate legal entity, subscribes to the capital stock of the respective FRB in an amount equal to 6 per cent of its own capital and surplus, adjusted each year as per the changes in capital and surplus of the member banks. Each FRB also maintains a ‘Surplus Account’ which is equivalent to the level of its respective capital.

  5. BoJ’s accounting rules require it to maintain a capital adequacy ratio of 10 ± 2% of its outstanding banknotes (Accounting rules of BOJ, Article 18).


Annex IV

Central banks with risk transfer mechanisms

(i) NCBs of the ESCB, for whom the losses caused by the ELA extended by the NCBs to the banks, are often seen to be guaranteed by the sovereign.

(ii) BoE, where the risk/returns of the QE programme are borne by the Treasury through a SPV.

(iii) BoK, for which any losses exceeding its reserves are to be borne by the Government, as provided for in the BoK’s statute.

(iv) SARB, where foreign-exchange profits or losses are borne by the Government while investment returns on foreign-exchange reserves and interest paid on foreign loans are accounted for in the central bank’s P&L account.

(v) RBNZ, where the financial consequences of forex interventions ordered by the Government are expressly borne by the Government.

(vi) RBI, for which the MSS enabled the sharing of sterilization costs between the Government (through the MSS) and the RBI (through OMOs).

(vii) BCdB, where the carrying cost of international reserves and the risks/rewards of forex swaps conducted in the domestic market are transferred to the Government.

(viii) US FED, which, in 2011, introduced an accounting change whereby losses incurred by it are to be treated as an asset representing a claim on the Treasury, which need to be offset before transfer of surplus to the Treasury can recommence. Further, the Treasury had also indemnified the US FED on its risk exposures arising from some of its bailout operations during the financial crisis;

(ix) Bank Indonesia and Banco Central de Reserva del Peru are instances where ad hoc RTMs were adopted during a period of crisis.


Annex V

Surplus distribution by an AE central bank

A major challenge which could arise for central banks having large forex holdings and taking valuation gains/losses to P&L, is that there would be considerable volatility in the P&L statement. This is best exemplified by the concerned central bank’s position in the post-GFC period.

This central bank has, consequently, put in a stringent surplus distribution policy involving a distribution reserve which contains profits that have not yet been distributed. It can also be used to offset against losses and can therefore also be negative.


Annex VI

Rating methodologies/ relevant ratings of Standard & Poor’s (S&P), Moody’s and DBRS

S&P

(i) S&P’s ‘monetary authorities rating methodology’ states that ‘The ratings on monetary authorities outside of monetary and currency unions are at the same level as their respective sovereign because we consider that they are analytically inseparable from one another’. This principle has been used by them to rate the Federal Reserve System (FRS), Federal Reserve Bank of New York (FRBNY), SNB, Sveriges Riksbank, and Bangko Sentral ng Pilipinas.

(ii) Incidentally, even though the US Sovereign and the FRS were already rated, S&P separately rated the FRBNY in 2010 (albeit at the same level of the Sovereign and the FRS). The S&P states in its rating paper that ‘in light of recent criteria updates, we believe it is useful to clarify that, in our opinion, the FRBNY, as one entity within the FRS, shares the FRS's credit quality. We are therefore explicitly assigning our ratings to reflect our view of the FRBNY's credit quality.’

(iii) In 1999, ratings were issued to the national central banks of Belgium, Finland, Ireland, Italy, Portugal, and Spain which were higher than those of their national governments based on the strong operational and policy making independence as per Maastricht Treaty and they being shareholders of ECB and part of monetary union.

(iv) The Sovereign rating methodology of S&P was updated in December 2017 to combine both the sovereign government and monetary authorities.

Moody’s

Moody’s has informed that in general they assign the same rating to central banks as the sovereign, given the important public policy role of central banks and have assigned ECB AAA ratings to have an anchor for their country ceilings for euro area member states.

DBRS

DBRS has also issued a rating methodology for central banks dated December 2013. While it specifies that the main factors for finalizing the rating include Sovereign creditworthiness, independence, performance, support and financial strength, it also mentions that sovereign rating is used to provide a preliminary assessment of a central bank’s creditworthiness and they may rate central banks above the level of the sovereign given its unparalleled financial flexibility and critical public policy and mandate.


Annex VII

Previously adopted methodologies for assessment of risk provisioning requirements of the RBI

Subrahmanyam Group (1997)

• CF plus ADF to be 12 per cent of assets of the RBI by the year 2005, subject to review, if considered essential

• Out of this 12 per cent, 5 per cent be earmarked for meeting shocks arising out of open market operations carried out by RBI under monetary policy operations assuming 10 per cent volatility in domestic assets.

• 5 per cent earmarked to absorb external shocks due to exchange rate volatility.

• General rule of thumb followed that internal reserves should be at least 25 per cent of the foreign assets to ascertain the exchange rate risk. Considering foreign assets as 40 per cent of total assets, the implied reserves required to absorb external sector shocks would be at least 10 per cent of total assets, of which EFR (equivalent to CGRA) constituted 5 per cent of total assets.

• The remaining 2 per cent was proposed towards systemic risk/developmental role. Out of this 2 per cent, 1 per cent was proposed to be earmarked and retained under ADF for meeting capital expenditure and investment in subsidiaries.

Malegam Committee (2014)

• RBI should maintain at all times buffers for risks which shall not be less than the amounts indicated below:

  1. For coverings risks of future rupee appreciation to FCA, 17 per cent of the carrying value of FCA but not less than the unrealized gains lying to the credit of CGRA.

  2. For gold price risk, 34 per cent of the carrying value of gold may be provided for. The total buffer for gold should not be less than the unrealized gain lying to the credit of GRA.

  3. For depreciation in the market value of foreign securities, buffer equivalent to the impact of the maximum jumps in yields in foreign currency holdings by the value of such holdings, converted into rupees based on a study of the risk of such investments and of the past volatility in yields.

  4. For risks arising out of depreciation in the market value of rupee securities to be at 7.5 per cent of the carrying value of investment in rupee securities.

  5. Operational risks and systemic risks to be provided as 15 per cent of the annual gross income of RBI.

• Since the balances in the CF and ADF are currently in excess of the buffers needed, there was no need to make any further transfers to CR and ADR for the next three years after which the position may be reviewed.

• The benchmarks for determining the level of reserves/buffers to be maintained should be reviewed at the end of each three-year period.

Economic Capital Framework (2015)

• Risk covered under ECF and assessment methodologies:

  1. Stressed Value at Risk engine to capture market risk. The risk parameters are 99.99 per cent CL, one year time horizon, 10-day return period, and Exponentially Weighted Moving Average (EWMA) decay factor of .995.

  2. Standardised Approach to capture credit risk.

  3. Basic Indicator Approach to capture operational risk.

  4. The ‘peak liquidity’ methodology adopted for capturing Contingency Risks. The methodology has been adapted to account for the low correlations between market risk and Contingency Risks.

• Components of equity under the ECF: Capital and Reserve Fund, risk provisions built up from retained earnings (CF and ADF) and revaluation balances.


Annex VIII

Constraints on monetization of revaluation balances by the RBI

Revaluation balances held by commercial entities can be monetized by selling the assets in case of need. This option may not, however, be open to central banks. RBI transferring ‘what it has not received’ could be seen as monetization of fiscal deficit. Also, the transfer of valuation balances is not permitted under the RBI General Regulations. Given that most of the revaluation balances represent gains made due to the depreciation of the rupee against the USD, trying to realize these revaluation gains would involve selling a substantial portion of the RBI’s USD assets which could result in the following:

(i) RBI’s intervention capabilities will be severely limited increasing forex vulnerability.

(ii) Unsustainable temporary rupee appreciation: the domestic forex market will not have the capacity to absorb the USD sales, which will:

  1. Impact the economy.

  2. Reduce RBI’s CGRA which is being monetized.

  3. Lead to selling of USD to realize CGRA which could result in realization of losses in IRA-FS.

(iii) Compromised monetary policy stance with severe liquidity and credit squeeze which will have an adverse impact on growth and stability.

(iv) Other central banks could have an issue with this, especially if their currency is impacted.

(v) The realized surplus will be used to retire GoI securities which will greatly reduce the RBI’s domestic portfolio, and thereby the effectiveness of monetary policy operations.

(vi) Increased reliance on MSS for monetary policy operations will lead to increasing fiscal expenditure.

(vii) Weakened RBI balance sheet: As the currency composition of the forex portfolio becomes highly skewed, RBI will become very vulnerable to a negative CGRA balance.

(viii) Substantially lower future RBI income as income generating assets will be sold to monetize valuation gains.

(ix) Moral hazard issue: Precedent will be set for using rupee depreciation funding fiscal expenditure.


Annex IX

An outline of the methodologies used in the ECF

Economic capital buffers

1. The EC has been defined as the difference between total assets and external liabilities of RBI. It includes Capital, Reserve Fund, CR, ADR, CGRA, IRA, FCVA and current year’s surplus. No hair cut is applied on the revaluation account balances.

Market Risk methodology

a) Concept of S-VaR introduced under Basel 2.5 has been used. Under S-VaR, current portfolio of the RBI is subjected to risk conditions of historically-identified stress period. The approach captures the diversification benefit of the consolidated portfolio i.e., forex, gold and G-Sec. (If correlation between two assets is less than 1, there will be diversification benefit in the form of reduced equity requirements.)

b) The VaR engine of the Department of External Investments and Operations (DEIO) was modified to serve as RBI’s S-VaR engine. Parameterisation of the S-VaR was modified in line with discussions with BIS officials.

c) Two scenarios were considered as stress period viz., a 10-year period ended August 2013 and a six-year period ended December 2008. The 10-year period ended August 2013 was taken for EC assessment.

d) Exponentially-Weighted Moving Average (EWMA) was used with a decay factor of 0.995 (used by BIS) to assign greater weightage to recent observations. A decay factor of 0.995 allows for a much longer data coverage and, therefore, ‘normalises’ the S-VaR vis-à-vis a decay factor of (say) 0.97 used in the DEIO model.

e) Time horizon for computing S-VaR was taken as one year. Though the capital planning for the RBI should be carried out with at least a medium-term perspective (say, 3-5 years), given the immediate challenges in risk modelling, the time horizon has been restricted to only one year. Further, the EC literature as well as available information on EC frameworks in commercial banks and CBs indicates that oneyear time horizon is taken for calculating EC.

f) To prevent over-estimation of risk while using one-year time horizon, 10-day return (instead of daily return) is used in the S-VaR engine. Parametric S-VaR is used instead of historic S-VaR since the lesser number of data points impacts historic SVaR.

g) Certain minor forex portfolios have not been inputted into S-VaR model due to technical reasons. These are normalized by applying the S-VaR percentages across entire forex, g-sec and gold portfolios. Loans and advances have not been covered under market risk.

h) The Basel 2.5 recommendation of using multiplication factor of 3 in S-VaR has not been applied to prevent over-estimation of risk in view of parameterisation.

Credit Risk methodology

3. Pending building up of a credit-VaR model, the Standardised Approach for credit risk under Basel II has been adopted, and the risk weights have been assigned accordingly to the RBI’s domestic and foreign exposures (viz., foreign commercial bank exposures and supranational exposures). For foreign sovereigns, we have used default probability of .01% for credit rating of AAA, .02% for AA+, etc. rated exposures, and applied 0.50 as Loss Given Default (LGD) to all foreign sovereign exposures. For the domestic exposures of the RBI, the exposure to the GoI were applied zero risk weight. In the absence of a credit-VaR model for the RBI, its Capital Adequacy Ratio was taken to be nine per cent for determining the capital charge for credit risk.

Operational Risk methodology

4. The Basel Basic Indicator Approach (BIA) has been adopted.

Liquidity Risk

5. Given the possibility of overlap of liquidity risk with the market risk, liquidity risk has not been included in the proposed EC framework at this stage. The one year time horizon for market risk is expected to cover this risk.

Correlation of balance sheet risks

6. Within market risk, the S-VaR engine takes into consideration various risk factors captured from the historical data and generates a 78 x 78 correlation matrix. With regard to correlation between market risk, credit risk and operational risk, other central banks are seen to assume this to be 1, as correlations can change considerably during stress periods. Hence, we have also assumed a correlation of 1 between market risk, credit risk and operational risk.

Contingent Risks arising from monetary and financial stability mandate

7. The following types of risks have been considered under the category of “Contingent Risk” of the Bank:

  1. Risks arising from ELA operations due to RBI’s LoLR role and its impact on balance sheet;

  2. Risks arising from sterilisation/ exchange rate operations and their impact on balance sheet; and

  3. Risks arising from monetary policy mandate for managing inflation risks

I) ELA operations (considered for Scheduled Commercial Banks only) and the associated risks:

a) To facilitate generation of various scenarios, liquidity shortage is simulated for scenarios ranging from the liquidity crisis affecting the top 5 networked banks to the entire banking system.

b) Maximum net daily liquidity injection (outstanding) by the RBI was INR 2.1 trillion (July 16, 2013). Since in severe crisis periods, the peak liquidity requirement may continue for several days, a period of ten days has been taken in this exercise.

c) SLR is assumed to be at 10% (over and above LCR). This is based on a medium-term assumption that with the introduction of the LCR, the SLR requirements will be brought down to broad international levels.

d) A 10% haircut/ margin has been assumed on the eligible collateral of commercial banks. It has been assumed that the banks would be required to meet the funding needs using their stock of liquid assets only and there will be no external/ market borrowing/ funding.

e) It is assumed that the RBI first provides the collateralised funding to commercial banks as per their requirements, and as the crisis escalates, ELA would be extended with relaxed collateral norms.

f) LoLR losses incurred by the RBI through ELA are assessed by assuming a recovery rate of 80% on the liquidity support on the poor-quality collateral. There is little experience of bank bankruptcies in India, but statistics from the USA show that recovery from bank bankruptcies is often high. A study of over 1,500 bank bankruptcies in the USA between 1984 and 2002 showed that the average degree of recovery was 79 percent.31

g) The capital charge will be converted into a metric of percentage of the combined banking sector balance sheet and going forward, this metric will be used for determining capital charge for determining ELA risk. Assessment of ELA risks for NBFCs, UCBs, etc. will be estimated as the model is refined in the days ahead.

h) ELA operations could be expected to have an expansionary impact on the balance sheet to the extent of liquidity provided under the ELA operations (in some scenarios up to 50% expansion). Further, during a period of financial stress, 15% Rupee depreciation is assumed (due to likely capital outflows), as well as concomitant USD 75 billion reduction in forex reserves on account of likely market interventions to reduce exchange rate volatility (which would lead to a contraction in balance sheet size). The underlying presumption is that in the face of a financial stability crisis, reducing exchange rate volatility through use of forex reserves would be a policy objective. Reckoning all these complex interlinkages (including depreciation of the Rupee also having an expansionary impact, movement of collateral into balance sheet in case of default, etc.) between the expansionary and contractionary impacts of ELA operations, a net 25% increase in balance sheet size is assumed for enhanced market risk. For the less severe ELA scenarios, forex reduction of USD 30 billion and 10% rupee depreciation is assumed.

i) Results of scenario analysis and correlations: The scenario analysis indicates a maximum capital charge of around 6.5% of balance sheet for the ELA risks of the Bank. The impact of Rupee depreciation (and the consequent rising CGRA balance) on ELA risks is simulated in the above exercise and its mitigating impact on the capital requirement is factored in and the correlation between market risk and ELA risk has been assumed to be low. The rationale for assuming a low correlation of ELA risk and appreciation of Rupee is that during a banking crisis, there could be capital outflow thereby putting pressure on the Rupee. However, given that the scenarios where ELA losses as well as valuation losses arise concurrently cannot truly be ruled out32, such scenarios are also taken into consideration while determining the overall size of the ‘Contingent Buffer’.

II) Risks arising from monetary policy/ sterilisation/ exchange rate operations:

a) Consequent to the responsibility of exchange rate management, the RBI has to maintain an adequate level of forex reserves (the issue of adequacy of forex reserves does not fall within the purview of this exercise). However, the RBI’s operations can quickly alter composition and size of the forex reserves, thereby changing its risk profile and capital requirement.

b) Further, a rapidly appreciating Rupee can force the RBI to intervene, increasing the size and currency mismatch of the balance sheet as well as depleting the CGRA. If liquidity absorption operations become warranted, there could be substantial decrease in the RBI’s income as OMO reduce holdings of G-Sec and interest outgo on account of reverse repo operations (though it may counteract to an extent the increased balance sheet size). This risk is mitigated to an extent by the MSS. Rising yields can also cause increased depreciation.

c) For this scenario, balance sheet expansion by 20% is simulated. Instances of high balance sheet growth during certain periods of forex inflows were 2007-08 (46%) and 2006-07 (23%). However a lower proxy has been used which incidentally is relatively close to the CAGR of 15.5% over the past 10 year period.

d) Periods of high inflows of 2003-04 and 2009-10 saw a fall of 38% and 46%, respectively, in income levels due in part to sterilisation costs. However, as MSS is available, a reduced fall in income of 10% is simulated.

e) These dynamic balance sheet risks, including earnings risks have been assumed (0.64) to have a strong, positive correlation with the market risk of the RBI (unlike the ELA risks) as these occur during times of Rupee appreciation.33

f) Though there will be an increase in the riskiness of the RBI balance sheet as currency mismatch increases, the SVaR percentage is kept constant as on balance sheet date.

Results of scenario analysis: The capital charge for these ‘contingent risks’ after adjusting for correlation with market risks is 3.5% of the balance sheet.

III) Monetary policy risks arising out of inflation management operations

a) The RBI as the monetary authority is responsible for managing inflation within the mandated levels. This mandate is to be implemented without concern to the impact of attendant risks on the RBI balance sheet. Managing high inflation would require raising policy rates which would, in turn, bring about a rising interest rate environment in the country, leading to depreciation in the G-Sec portfolio.

b) This impact is modelled looking at yield jumps which are over and above those provided for in the VaR estimations. On the other hand, high inflation would also cause a depreciation (say, 15%) in the Indian Rupee thereby building CGRA valuation buffers, but also necessitating market interventions which could lead to a decline in the level of forex reserves (say, USD 75bn). Thus, the net increase in CGRA will be offset by the valuation losses caused by rising yields on G-Sec, to arrive at the capital charge required.

Results of simulation: The Rupee appreciation and high inflation would be negatively correlated. We have, therefore, in view of our market risk provision, taken the capital charge for inflation risk as zero.

IV) Size of Buffer for Contingent Risk

We observe that the maximum capital charge for ELA risk is 6.5% of balance sheet size, though it is difficult to assign probability for the occurrence for a financial stability crisis. However, this probability as well as correlation with market risk is non-zero given other central bank’s experience in this regard. The capital charge for sterilisation risk (which has high correlation with market risks) works out to be 3.5%. In view of the above, a contingent-risk buffer of 4% of balance sheet is recommended over the medium term. This target may be periodically reviewed (say, every 5 years).


Annex X

Risk Tolerance Statement (Risk Philosophy) of the Reserve Bank of India

“The Reserve Bank of India (‘Bank’), in pursuit of its core objectives of fostering monetary and financial stability conducive to sustainable economic growth, and to ensure the development of an efficient and inclusive financial system, is exposed to considerable risks including policy, strategic, reputational, financial, and operational risks. The Bank is a risk-sensitive institution and recognizes that failure to effectively manage these risks may adversely impact the achievement of its core objectives.

The Bank, therefore, seeks to manage its risks appropriately, consistent with the risk tolerance limits articulated from time to time:

  • The Bank takes a considered view on policy and strategic risks, which are managed through institutional frameworks aimed at effectiveness, transparency, and accountability.

  • The financial risks arising out of policy and market operations are accepted as significant by the Bank.

  • The financial risks of reserves management are addressed within a framework of safety, liquidity, and returns.

  • The Bank has a low tolerance for operational risks, which are sought to be minimized.

As financial risk considerations remain subordinate to the Bank's public policy objectives, adequate provision is sought to be built to absorb the risks that could materialize from various eventualities.”


Annex XI

Expansion of eligible assets classes by select central banks following the GFC

Central Bank Public securities Private assets
Domestic Foreign Corporate bond ABS Short-term bank debt Bank loans
Fed–OMO Eligible Added Added Added Added Added
-Standing facility Eligible Eligible Eligible Eligible Eligible Eligible
ECB Eligible Added Expanded Eligible Expanded Eligible
BoE Eligible Expanded Added Added Not eligible Not eligible
BoJ Expanded Added Expanded Expanded Not eligible Expanded
BOC – OMO Expanded Added Added Not eligible Added Added
- Standing facility Eligible Added Eligible Not eligible Eligible Added
RBA Eligible Not eligible Added Added Added Not eligible
Eligible: Continuing from prior to crisis and no change; Added: Made eligible during the crisis; Expanded: Indicates the asset class has been eligible since pre-crisis and eligible type of security was expanded during the crisis; Not eligible: asset class has continued to be ineligible through the crisis period.
Source: IMF Paper (2010) of Monetary and Capital Markets Department; Approved by José Viñals

Annex XII

Recapitalization of commercial banks by national treasuries

Country Bank Recapitalization support
US Bank of America USD 15 bn
  Citigroup USD 45 bn
  Merrill Lynch USD 30 bn
  Wachovia USD 10 bn
Belgium Dexia EUR 8.5 bn
Switzerland UBS CHF 6 bn
Germany Aareal Bank AG EUR 0.5 bn
  Commerzbank AG EUR 18.2 bn
  West LB AG EUR 3 bn
UK Banking sector (4) Pounds 137 bn
Netherlands Fortis EUR 19.8 bn
  ING EUR 10 bn
Ireland AIB EUR 5.5 bn
  Bank of Ireland EUR 5.5 bn
  Anglo EUR 5.5 bn
(Source: 1) The European Union’s Response to the 2007-2009 Financial Crisis, Walter W. Eubanks, August 13, 2010; 2) Recapitalisation of failed banks – some lessons from the Irish experience, Address by Mr Patrick Honohan, Governor of the Central Bank of Ireland, at the 44th Annual Money, Macro and Finance Conference, Trinity College, Dublin, 7 September 2012; 3) Historical Losses and Recapitalisation Needs Findings Report, Financial Stability Board, 9 November 2015; 4) Bank rescues of 2007-09: outcomes and cost, Federico Mor, Briefing Paper, House of Commons, Number 5748, October 8, 2018)

Annex XIII

Projection of RBI’s balance sheet and net income till 2022-23

(1) Dataset on RBI’s balance sheet size, net income and net foreign assets: 1990-91 to 2018-19

(in ₹ Billion)
Table AXIII.1: RBI’s balance sheet size, net income and net foreign assets
Year Balance Sheet (BS) Net Foreign Assets (NFA) Net Income (NI) NFA-to- BS ratio NI-to- BS ratio
(1) (2) (3) (4) (3) / (2) (4) / (2)
1990-91 1236 98 33 0.08 0.03
1991-92 1426 251 24 0.18 0.02
1992-93 1618 324 15 0.20 0.01
1993-94 1812 620 34 0.34 0.02
1994-95 2182 733 51 0.34 0.02
1995-96 2355 744 76 0.32 0.03
1996-97 2503 1051 95 0.42 0.04
1997-98 2933 1146 93 0.39 0.03
1998-99 3365 1442 147 0.43 0.04
1999-00 3600 1641 166 0.46 0.05
2000-01 4075 2084 163 0.51 0.04
2001-02 4536 2836 181 0.63 0.04
2002-03 5198 3822 165 0.74 0.03
2003-04 6098 5436 66 0.89 0.01
2004-05 6828 5953 122 0.87 0.02
2005-06 8088 7472 205 0.92 0.03
2006-07 10020 8676 682 0.87 0.07
2007-08 14630 13381 517 0.91 0.04
2008-09 14082 12644 525 0.90 0.04
2009-10 15531 12571 245 0.81 0.02
2010-11 18047 13790 284 0.76 0.02
2011-12 22089 15944 430 0.72 0.02
2012-13 23907 16535 618 0.69 0.03
2013-14 26244 18770 527 0.72 0.02
2014-15 28892 22575 669 0.78 0.02
2015-16 32430 24455 669 0.75 0.02
2016-17 33041 25004 439 0.76 0.01
2017-18 36176 27791 642 0.77 0.02
2018-19 41029 29527 - 0.72 ..

(2) Growth rate of RBI’s balance sheet and net income across various periods

Table AXIII.2: Growth rate of RBI’s balance sheet and net income across various periods
Period No. of years Balance Sheet growth rate
(A)
Net Income growth rate
(B)
2014-15 to 2018-19 5 9.16% 11.11%
2009-10 to 2018-19 10 11.40% 17.18%
2004-05 to 2018-19 15 13.67% 16.36%
1999-00 to 2018-19 20 13.66% 10.02%
1994-95 to 2018-19 25 13.00% 13.33%

(3) Chow-test Results

Given the sharp structural change in the balance sheet composition over the years, a Chow-test was done to identify structural breaks in the series based on net foreign asset- to- balance sheet ratio, so as to identify the period for the purpose of projection. The test results (Table AXIII.2) indicate a break point during 2000-01. Therefore, data from 2000-01 onwards was considered for further analysis.

Table AXIII.3: Chow Test
Chow Breakpoint Test: 2000-01
Null Hypothesis: No breaks at specified breakpoints
Varying regressors: All equation variables
Equation Sample: 1991-92 to 2018-19
F-statistic 2.83663   Prob. F(2,24) 0.0784
Log likelihood ratio 5.94139   Prob. Chi-Square(2) 0.0513
Wald Statistic 5.673261   Prob. Chi-Square(2) 0.0586

(4) Balance sheet projection

Balance sheet (BS) projection is done using an autoregressive model, AR(2). The fitted model is,

The projected balance sheet, in accordance with the aforementioned model is given in table AXIII.4.

Table AXIII.4: Projected balance sheet of RBI till 2022-23
(in ₹ Billion)
Year Projected Balance Sheet size
2019-20 44,915
2020-21 49,166
2021-22 53,663
2022-23 58,420

The lag length of AR model was chosen based on the residual diagnostics, which indicate that two lags are optimal. In particular, the Breusch-Godfrey serial correlation LM test was done to test the null hypothesis, H0: No serial correlation of residuals upto 2 lags. The test results (Table AXIII.2) yielded a p-value of 0.545 (higher than 0.05), by which there is no reason to reject the null hypothesis, showing that there is no serial correlation left in the residuals. Further, the Correlogram Q-Statistics also confirm this.

Forecast efficiency: To examine forecast efficiency, the model was worked out based on data from 2000-01 to 2015-16 for out-of-sample forecast which was used to forecast values for 3 years from 2016-17 to 2018-19. The measures of out-of-sample forecasts are given below. The root mean square error (RMSE) for the out-of-sample forecasts is 0.075, which is quite low and closer to the RMSE for in-sample forecast efficiency, 0.078.

(5) Projection of Net Income

Net Income is projected using net income-to- balance sheet ratio for the period 2000-01 to 2018-19 and the projected values of balance sheet as provided in table AXIII.4.

Table AXIII.5: Net income- to- balance sheet ratio
Mean 0.0265
SD 0.0131
Mean - 1SD 0.0134
Mean + 1SD 0.0395

The projected net income using the mean net income to balance sheet ratio is given in table AXIII.6

Table AXIII.6: Projected net income of RBI till 2022-23
(in ₹ Billion)
Year Using mean net income to balance sheet ratio Using mean (-) 0.5 SD net income to balance sheet ratio
2019-20 1190 896
2020-21 1303 981
2021-22 1422 1071
2022-23 1548 1165

Table AXIII.7: Projected risk provisioning with immediate move to target realized equity of 5.5% of BS
Year Mean Mean-0.5 SD Mean+0.5 SD Mean-1 SD Mean+1 SD
2018-19 0% 0% 0% 0% 0%
2019-20 18% 24% 14% 36% 12%
2020-21 18% 24% 14% 35% 12%
2021-22 17% 23% 14% 34% 12%
2022-23 17% 22% 14% 33% 11%
Average for 2019-23 14%*
(18%)#
19%
(23%)
11%
(14%)
28%
(35%)
9%
(12%)
*This represents the average risk provisioning for the five year period of 2018-19 to 2022-23 including zero per cent risk provisioning for 2018-19
# This represents the average risk provisioning for the four year period of 2019-20 to 2022-23 excluding zero per cent risk provisioning for 2018-19

Table AXIII.8: Projected risk provisioning with a gradual glide down of target realized equity from 6.5% to 5.5% of BS
Year Mean Mean-0.5 SD Mean+0.5 SD Mean-1 SD Mean+1 SD
2018-19 0% 0% 0% 0% 0%
2019-20 12% 16% 9% 23% 8%
2020-21 11% 15% 9% 22% 7%
2021-22 10% 13% 8% 19% 6%
2022-23 8% 11% 7% 16% 6%
Average for 2019-23 8%*
(10%)#
11%
(14%)
7%
(8%)
16%
(20%)
5%
(7%)
*This represents the average risk provisioning for the five year period of 2018-19 to 2022-23 including zero per cent risk provisioning for 2018-19
# This represents the average risk provisioning for the four year period of 2019-20 to 2022-23 excluding zero per cent risk provisioning for 2018-19

Table AXIII.9: Projected risk provisioning with immediate move to target realized equity of 6.5% of BS
Year Mean Mean-0.5 SD Mean+0.5 SD Mean-1 SD Mean+1 SD
2018-19 0% 0% 0% 0% 0%
2019-20 21% 28% 17% 42% 14%
2020-21 21% 28% 17% 42% 14%
2021-22 21% 27% 16% 41% 14%
2022-23 20% 27% 16% 39% 13%
Average for 2019-23 17%*
(21%)#
22%
(28%)
13%
(17%)
33%
(41%)
11%
(14%)
*This represents the average risk provisioning for the five year period of 2018-19 to 2022-23 including zero per cent risk provisioning for 2018-19
# This represents the average risk provisioning for the four year period of 2019-20 to 2022-23 excluding zero per cent risk provisioning for 2018-19

Table AXIII.10: Projected risk provisioning under various scenarios
Net income to balance sheet ratio scenarios Illustrative average rate of risk provisioning as per cent of net income from 2018-19 to 2022-23 under various scenarios*
Uniform CRB target of 6.5 per cent of balance sheet till 2022-23 CRB target of 6.5 to 5.5 per cent of balance sheet under a gradual glide path till 2022-23 Uniform CRB target of 5.5 per cent of balance sheet till 2022-23
Mean 16.6 (20.7) 8.1 (10.1) 14.0 (17.5)
Mean + 0.5 SD 13.3 (16.6) 6.5 (8.1) 11.3 (14.1)
Mean – 0.5 SD 22.0 (27.5) 10.8 (13.5) 18.6 (23.3)
Mean + SD 11.1 (13.9) 5.4 (6.8) 9.4 (11.7)
Mean – SD 32.8 (41.0) 16.0 (20.0) 27.8 (34.7)
* Given that the risk provisioning could be low during 2018-19, the figure in parenthesis represent the risk provisioning required in the remaining 4 years

Select Abbreviations and Definitions

ADF Asset Development Fund
AE Advanced Economies
AvE Available Equity
ARE Available Realized Equity
BCBS Basel Committee on Banking Supervision
BCdB Banco Central do Brasil
BCdC Banco Central de Chile
BdF Banque de France
BIA Basic Indicator Approach
BIS Bank for International Settlements
BNM Bank Negara Malaysia
BoE Bank of England
BOFC Bank Otkritie Financial Corporation PJSC
BoJ Bank of Japan
BoK Bank of Korea
BoP Balance of Payment
BoR Bank of Russia
bps Basis Points
CAGR Compound Annual Growth Rate
CB Central Bank
CF Contingency Fund
CGRA Currency and Gold Revaluation Account
CL Confidence Level
CPI Consumer Price Index
CRA Credit Rating Agency
CRB Contingent Risk Buffer
CRR Cash Reserve Ratio
DBRS Dominion Bond Rating Service
DEIO Department of External Investments & Operations
DICGC Deposit Insurance and Credit Guarantee Corporation
EC Economic Capital
ECB European Central Bank
ECF Economic Capital Framework
EFR Exchange Fluctuation Reserve
ELA Emergency Liquidity Assistance
EMDE Emerging Market and Developing Economy
EWMA Exponentially Weighted Moving Average
ERM Enterprise Risk Management
ES Expected Shortfall
ES(N) Expected Shortfall(Normal)
ESCB European System of Central Banks
FCA Foreign Currency Assets
FCVA Foreign Exchange Forward Contracts Valuation Account
FEMA Foreign Exchange Management Act
FER Foreign Exchange Reserves
FIT Flexible Inflation Targeting
FRB Federal Reserve Bank
FRBM Act Fiscal Responsibility and Budget Management Act
FRBNY Federal Reserve Bank of New York
FRS Federal Reserve System
GDP Gross Domestic Product
GFC Global Financial Crisis
GoI Government of India
GRA Gold Revaluation Account
G-sec Government of India securities
HHI Hirschman-Herfindahl Index
HMT Her Majesty’s Treasury
HQLA High Quality Liquid Assets
IBC Insolvency and Bankruptcy Code
IFRS International Financial Reporting Standards
IIFCL India Infrastructure Finance Company Limited
IIP International Investment Position
IMF International Monetary Fund
IRA-RS Investment Revaluation Account-Rupee Securities
IRA-FS Investment Revaluation Account-Foreign Securities
LAF Liquidity Adjustment Facility
LCR Liquidity Coverage Ratio
LGD Loss Given Default
LIBOR London Inter-bank Offer Rate
LoBoM Lower of Book or Market
LoLR Lender of Last Resort
MAS Monetary Authority of Singapore
MMLR Market Maker of Last Resort
MSS Market Stabilisation Scheme
MTM Marked to Market
NABARD National Bank for Agriculture and Rural Development
NBFC Non Banking Financial Company
NCB National Central Banks
NDA Net Domestic Assets
NDTL Net Demand and Time Liabilities
NFA Net Foreign Assets
NHB National Housing Bank
NPA Non-Performing Assets
NRI Non-Resident Indian
OMO Open Market Operations
Op risk Operational Risk
P&L Profit and Loss
PFCVA Provision for Forward Contracts Valuation Account
PPPP Principle of Public Policy Predominance
PSB Public Sector Banks
QE Quantitative Easing
RBA Reserve Bank of Australia
RBI Reserve Bank of India
RBNZ Reserve Bank of New Zealand
RBRF Reserve Bank Reserve Fund
RTL Risk Tolerance Limit
RTM Risk Transfer Mechanism
S&P Standard & Poor’s
SARB South African Reserve Bank
SDF Standing Deposit Facility
SDR Special Drawing Rights
SIDBI Small Industries Development Bank of India
SLR Statutory Liquidity Ratio
SNB Swiss National Bank
SPV Special Purpose Vehicle
SSDP Staggered Surplus Distribution Policy
S-VaR Stressed Value at Risk
TALF Term Asset-Backed Securities Loan Facility
TER Total Equity Required
ToR Terms of Reference
UCB Primary (Urban) Co-operative Bank
UIP Uncovered Interest-rate Parity
US Fed US Federal Reserve
VaR Value at Risk

Select Definitions in context of the RBI’s ECF:

Economic capital / Risk buffers The RBI’s risk equity comprising of its Capital, Reserve Fund, risk provisions [Contingency Fund (CF) and Asset Development Fund (ADF)], and revaluation balances (CGRA, IRA-RS, IRA-FS and FCVA).
Risk provisions/ Realized risk provisions/ Retained earnings Provisions made towards CF and ADF under Section 47 of the RBI Act.
Realized equity/ Available realized equity (ARE) The component of RBI’s economic capital comprising its Capital, Reserve Fund and risk provisions (CF and ADF)
Requirement for realized equity (RRE) The Contingent Risk Buffer plus any shortfall in revaluation balances vis-à-vis their target requirement.
Contingent Risk Buffer (CRB) Component of RBI’s economic capital required to cover its monetary and financial stability, credit and operational risks.
Revaluation balances The unrealized gains, net of losses resulting from exchange rate, gold price and interest rate movements, on account of periodic marking to market of RBI’s foreign currency assets, gold, foreign dated securities and rupee securities
Capital Paid-up capital in accordance with section 4 of the RBI Act, 1934 (Notes to Accounts [XII.5.1(i)] in RBI’s Annual Report 2017-18)
Reserve Fund Reserve Fund of ₹ 5 crore provided for in terms of Section 46 of the RBI Act which was supplemented with the valuation gains which accrued on account of an amendment to Section 33 (4) of the RBI Act in 1990-91 (Notes to Accounts [XII.5.1(ii)] in RBI’s Annual Report 2017-18)
Contingency Fund Provisions for meeting unexpected and unforeseen contingencies, including depreciation in the value of securities, risks arising out of monetary/ exchange rate policy operations, systemic risks and any risk arising on account of the special responsibilities enjoined upon the RBI (Notes to Accounts [XII.5.1(v)(a)] in RBI’s Annual Report 2017-18)
Asset Development Fund Provisions for investments in subsidiaries and associated institutions and to meet internal capital expenditure (Notes to Accounts [XII.5.1(v)(b)] in RBI’s Annual Report 2017-18)
CGRA Unrealized gains/losses on Foreign Currency Assets and gold due to movement in exchange rate and prices of gold (Notes to Accounts [XII.5.1(v)(c)] in RBI’s Annual Report 2017-18)
IRA- Foreign Securities Unrealized gains/losses on foreign dated securities on periodic revaluation (Notes to Accounts [XII.5.1(v)(d)] in RBI’s Annual Report 2017-18)
IRA- Rupee Securities Unrealized gains/ losses on rupee securities on periodic revaluation (Notes to Accounts [XII.5.1(v)(e)] in RBI’s Annual Report 2017-18)
FCVA Unrealized gains/ losses on outstanding forward contracts (Notes to Accounts [XII.5.1(v)(f)] in RBI’s Annual Report 2017-18)
Net income Gross income net of expenditure, prior to risk provisioning.

1 RBI will be required to change level of its NDA in case of change in its capital towards achievement of its monetary policy objectives

2 Financial resilience is defined here as the financial resources or RTMs available to a central bank for absorbing/ transferring losses to the government so as to ensure the efficacy of its policy actions and that the operations are not compromised by financial losses.

3 This is best exemplified by the Bank of Korea (BoK), whose statute provides that while BoK shall not have capital, it shall retain 30 per cent of any net profit as reserves, with the provision for automatic (ex post) recapitalization in case losses exceed the amount of reserves.

4 As per Bindseil, the required level of positive capital for ensuring good inflation performance will depend on the risks in the central bank balance sheet and on contingent liabilities, i.e., possible off-balance sheet obligations

5 BoE’S calculation of seigniorage income: Members of the Note Circulation Scheme buy new banknotes from the BoE at face value. This money is invested in assets such as government bonds. The cost of printing and issuing banknotes is deducted from the income on these assets, and the balance is returned to the Treasury as seigniorage. Bank of Canada’s calculation of seigniorage is the difference between the interest Bank of Canada earns on a portfolio of Government of Canada securities—in which it invests the total value of all bank notes in circulation—and the cost of issuing, distributing and replacing those notes.

6 We refer to equity and economic capital synonymously in this report to include capital, reserves, risk provisions and revaluation balances.

7 The transfer of capital by national central banks (NCBs) of the Euro-system to ECB was a gradual 3-year process during which €3.49 billion was paid-up by Euro area NCB’s, increasing their contribution to ECB’s capital from €4.14 billion to €7.63 billion. Accordingly, ECB’s subscribed capital and paid-up capital as on December 31, 2018 was €10.82 billion and €7.74 billion respectively.

8 The capital increase was deemed appropriate in view of increased volatility in exchange rates, interest rates and gold prices as well as credit risk. As the maximum size of the ECB’s provision and reserves is equal to the level of its paid-up capital, this decision allowed the governing council to augment the provisions by an amount equivalent to the capital increase, starting with the allocation of part of that year’s profit. From a longer-term perspective, the increase in capital is also motivated by the need to provide an adequate capital base in a financial system that has grown considerably.

9 Stress testing, thereafter, replaced S-VaR in 2017.

10 While BCBS standards are applicable to commercial banks, in the absence of an international benchmark for risk methodologies for central banks, the guidelines recommended by the BCBS are also broadly looked at by central banks with suitable modifications to meet their specific central banking needs.

11 The ESCB is an important exception which while having adopted IFRS accounting norms has not adopted the requirement of taking valuation gains and losses of their forex and gold portfolio to P&L.

12 For instance, ECB does not provide fungibility between revaluation balances of different assets/instruments; the RBNZ excludes valuation balances while assessing their adequacy of risk buffers.

13 In the pre Fiscal Responsibility and Budget Management (FRBM) phase, a change in SLR had a concomitant impact on net RBI credit to the Government, which would then have an impact on the balance sheet. With the RBI being prohibited under the FRBM Act, 2003 to participate in primary market auctions, the impact on the balance sheet is through RBI’s participation in the secondary market.

14 Hedging could also necessitate the RBI taking a view on the probable level of the rupee. This view is not in consonance with the RBI’s exchange rate management policy which is aimed at managing volatility in the exchange rate without reference to a target rate or band. Further, the cost of hedging would be prohibitive.

15 The RBI’s currency risk is the case of ‘good losses-bad profits’ as it occurs due to appreciation of the rupee against other currencies, the prominent being the USD. As the foreign exchange reserves have been invested over a diversified currency-wise portfolio, the appreciation of rupee against the other currencies held in the portfolio could also lead to losses.

16 Given that the RBI’s balance sheet is denominated in Indian rupee, its forex reserves are translated from the numeraire currency to Indian rupee at the applicable rates. Thus, were the rupee to appreciate from ₹ 69/ dollar to ₹ 67/ dollar, the rupee equivalent of the forex reserves would fall by ₹ 2/ dollar. This valuation loss is reflected in the balance sheet as a reduction in the CGRA. Conversely, if the rupee were to depreciate against the dollar from USD-INR 69 to USD-INR 71, the rupee equivalent of the forex reserves would gain by ₹ 2/ dollar. This would be reflected by an increase in the CGRA by a corresponding equivalent.

17 This essentially means that the CGRA can be used not only for meeting the risks of USD-INR, cross-currency and gold price movements, but also for interest rate risks. Similarly, the IRA-RS and IRA-FS can meet currency and gold price risks in addition to the interest rate risks. This approach reduces the capital requirement of the RBI vis-à-vis the accounting approach used by the Malegam Committee. This also brings a certain divergence in the financial resilience of the RBI’s balance sheet, as reflected under the accounting policies and as assessed under the EC methodologies, as the former does not permit fungibility and the IRA and CGRA cannot be used interchangeably

18 In the paper ‘Paranoia or Prudence? How Much Capital Is Enough for the RBI?’, Arvind Subramanian, et al. (2018) have estimated the capital of RBI to be 27.7 per cent of the balance sheet after including the capital, Reserve Fund, CF, ADF, revaluation balances and other components such as provision for payables, Gratuity and Superannuation Fund and Miscellaneous mentioned under Schedule 3 of Reserve Bank’s Notes to Accounts. In the paper ‘Central Bank Equity: Facts and Analytics’, Lahiri et al. have estimated the capital of RBI to be 6.60 per cent by including Capital, Reserve Fund and CF.

19 For instance, if after retaining 30 per cent net income during a particular year, the risk provisions at the end of the next year continue to be below the desired levels and are, say, at a level requiring risk provisioning of 30 per cent again, then in the second year, 40 per cent of the net income will be retained as risk provisions.

20 Incidentally, the RBI is possibly the only central bank using 10-day return which had been developed so as to prevent over-estimation of risk at 99.99 CL.

21 In 2017–18, transferable surplus as per ECF was ₹385.1 billion. However, the surplus transferred was ₹500 billion. Thus, over the relevant period, the transferable surplus proposed by the ECF-SSDP combine was ₹2,536.2 billion (86.1 per cent of net income) against an actual surplus transfer of ₹2651.1 billion (90 per cent)

22 The first period has been extended to seven years to align the starting year with the implementation of the recommendations of the Subrahmanyam Group (1997).

23 Bank of England (2018). ‘Financial relationship between HM Treasury and the Bank of England: Memorandum of Understanding.’ Retrieved from https://www.bankofengland.co.uk/-/media/boe/files/memoranda-of-understanding/financial-relationship-between-hmt-and-the-boe-memorandum-of-understanding.pdf

24 European Central Bank (2010). ‘ECB increases its capital’ Press release dated December 16, 2010. Retrieved from  https://www.ecb.europa.eu/press/pr/date/2010/html/pr101216_2.en.html

25 The current stress being experienced by the NBFC sector, for example, led to calls for appropriate LoLR action by the RBI.

26 An AE central bank, in its 2009 annual report, highlights the role of Sovereigns and central banks in supporting the real estate and securitization markets and mentions that ‘measures taken in the US included the Term Asset-Backed Securities Loan Facility (TALF), a programme created for investors in securitisations, purchases of mortgage-backed securities and measures to cut down on foreclosures. As a result, financial and securitisation markets in the US and Europe gradually recovered. Real estate markets also stabilised, but remained fragile’.

27 The ‘peak liquidity’ methodology is adopted for capturing LoLR risks wherein liquidity shortage is simulated for scenarios ranging from the liquidity crisis affecting the top five networked banks to the entire banking system. The maximum net daily injection of INR 2.1 trillion done on July 16, 2013 was assumed to be peak liquidity requirement for 10 days. SLR was assumed to be 10 per cent over LCR and a 10 per cent haircut was assumed on eligible collateral. While no losses are assumed on lending against good quality collateral, 80 per cent recovery rate (20 per cent loss) is assumed on ELA after good quality collateral is exhausted.

28 RBI will be required to change level of its NDA in case of change in its capital towards achievement of its monetary policy objectives

29 Given the large size of India’s GDP, this will not have a material impact on its debt-GDP ratio. For instance, even if a hypothetical amount of ₹1 trillion was identified, this would amount to only 0.6 per cent of the GDP. While India’s current debt-GDP ratio is 68.3 per cent, S&P’s relevant band for debt-GDP ratio is 60 to 80 per cent of GDP.

30 After excluding the profit from the sale of State Bank of India shares to Government from income.

31 Resolution Costs and the Business Cycle, Kathleen McDill, FDIC Working Paper 2004-01, March 2004

32 One way of addressing this correlation issue would be to take the higher of the two capital charges (capital for market risk or ELA risk). However, if one takes a medium-term or long-term view, two scenarios which can give rise to both ELA losses as well as valuation losses cannot truly be ruled out:
i.) Large capital inflows (causing Rupee appreciation and valuation losses for the RBI) feeds asset bubbles in the economy, and in a hard landing that may follow causes ELA losses for the RBI.
ii.) A reverse chain of occurrence i.e. losses caused by financial stability crisis, followed by valuation/ sterilisation losses as the economy stabilises/ strengthens
In fact, the latter scenario is known to have caused Banco Central de Chile to go into negative equity position in the 1980’s and continues to be so.

33 During 2002-03 to mid-2008-09, a period of large capital inflows, the correlation coefficient between MSS/OMO and intervention in the foreign exchange market was .64. (Source: Determinants of Liquidity and the Relationship between Liquidity and Money: A Primer. A. K. Mitra and Abhilasha. RBI WP 2012).

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Manual on the Compilation of Flow of Funds Accounts of the Indian Economy

Contents
A. Introduction
1. Overview
2. Institutional arrangements
4. Institutional Units
5. Institutional Sectors
6. Financial Assets and Liabilities (Instruments)
7. Sources of data
8. Balancing the accounts
a) Consistency between financial and non financial accounts
b) Stock/flow consistency
c) Consistency checks/Control Totals
B. Compilation Guide
1. Total Economy
1.1 Non-Financial Corporations
1.1.1. Non-Government non-financial companies
Liabilities
Deposits
Debt Securities
Loans (Borrowings)
Equity (Paid-up capital)
Trade payables and other current liabilities
Reserves and Surplus
Provisions
Financial Assets
Currency and deposit
Investments (Debt and equity securities)
Loans and advances
Other non current assets
Non-Financial Assets
Net fixed assets
Inventories
2.2.2 Power Sector Companies and State Electricity Boards
Financial Liabilities
Deposits
Debt securities (bonds and debentures)
Loans (Borrowings)
Other accounts payable
Financial Assets
2.2.3 Co-operative non-credit societies
2.2.4 Port Trusts
Liabilities
Assets
2.2.5 Government Non-departmental non-financial undertakings
(i) Central and State Government Companies
Liabilities
Financial Assets
1.2 Financial Corporations
1.2.1 Central Bank – the Reserve Bank of India
Liabilities
Currency and deposits
Equity and investment fund shares
Other accounts receivable/payable
Reserve funds & other funds
Financial Assets
Monetary gold and SDRs
Deposit-taking Corporations, except the Central Bank
Commercial Banks
Liabilities
Equity and Investment Fund Shares
Financial Assets
Co-operative Banks
State and Central Co-operative Banks
Financial Assets
Urban Cooperative Banks (UCBs)
Deposit-Taking Non-Banking Financial Companies (NBFC-D)
Deposits
Debt securities
Loans (Borrowings)
Currency and deposits
Debt securities
Loans and advances
Equity and Investment fund shares
Other accounts receivable / payable
Deposit-Taking Housing Finance Companies (HFC-D)
1.2.3 Mutual Funds
1.2.4 Other financial intermediaries, except insurance corporations and pension funds
Primary Credit Societies
State Cooperative Agriculture and Rural Development Banks (SCARDBs) and Primary Cooperative Agriculture and Rural Development Banks (PCARDBs)
Industrial Co-operative Banks (State/Central)
Financial Corporation and Companies
1.2.6 Insurance corporations
1.2.7 Pension funds
Non- government Provident Funds
2.3 General government
2.3.1 Central government including social security
Liabilities
Financial Assets
Currency and deposits
Loans & Advances
Equity and Investment Fund shares (Investments)
Other accounts receivable
2.3.2 State government and Union Territories including social security
Liabilities
Debt Securities (Market Loans)
Loans (Borrowings)
Provident funds
Financial Assets
Currency and deposits
Loans and Advances
Investments (Debt and Equity)
2.3.3 Local government including social security
2.4 Rest of the World
2.5 Household and Non-profit Institutions serving Household Sectors
List of Formats
Format 1: List of Financial Instruments adopted in the Flow of Funds Accounts
Abbreviations

A. Introduction

1. Overview

1. The objective of this Manual is to provide a comprehensive guide on the extant methodology adopted for the compilation of the Flow of Funds (henceforth, FOF) Accounts for the Indian economy. The Manual is a supplement to the Report of the Working Group on the compilation of FOF Accounts, 2015 (Chairman: Shri Deepak Mohanty). All the recommendations of the Working Group that are implementable at the present juncture have been incorporated in the Manual.

2. The Manual would be updated on a periodic basis taking into account evolving changes in the national accounting framework and new data/data sources in line with the remaining recommendations of the Working Group. In view of this, this Manual and the subsequent revisions would be released as a web version.

3. It may be recalled that the Reserve Bank of India (RBI) is entrusted with the compilation of the annual FOF accounts on a 'from whom-to-whom‘ basis and the RBI has, since 1964, published the FOF accounts for the Indian economy from the year 1951-52 onwards. The FOF accounts for India owe their origin to a suggestion of Sir C.D. Deshmukh, the then Union Finance Minister, in 1955. In 1956, the CSO initiated preparatory work along with the RBI. In 1959, Professor H. W. Arndt of the University of Australia carried out a study in consultation with CSO, Ministry of Finance and RBI, on FOF accounts. The memorandum entitled ''Financial Flows in the Indian Economy 1951-52 – 1957-58'', prepared by Prof. Arndt was discussed by representatives of the CSO and the RBI. They, in turn, referred the issue to a Working Group on Flow of Funds (Chairman: Shri P. C. Mathew) to formulate proposals for further work in this direction.

4. The Mathew Working Group took note of statistics then available and the important work done by Prof. H.W. Arndt and suggested a model set of accounts to be adopted. The compilation of the detailed FOF accounts for the Indian economy was then initiated in 1959 under the joint auspices of the Central Statistics Office (CSO) and the RBI. The Report of the Mathew Working Group, published in 1963, presented a consolidated FOF accounts for the year 1957-58. Subsequently, the RBI developed the work further and published the detailed FOF accounts since December 1964 starting with the data from 1951-52 onwards. The sectoral classification and financial instruments covered were in line with the recommendations of the Mathew Working Group. Since then, these accounts are being published periodically in the monthly RBI Bulletin with the scope of the accounts extended over the years either by way of coverage, i.e., inclusion of additional sub-sectors or by the use of more refined methods of estimation and classification of sub-sectors.2

5. A Manual on the methodology of compilation of the FOF was published as a supplement to the December 1988 issue of the Reserve Bank of India Bulletin (henceforth, the 1988 Manual). This was followed by subsequent changes regarding certain data sources and methodology of estimation which were published along with the FOF articles in the January 1991 and January 1992 issues of the RBI Bulletin. In March 2007, the RBI prepared a 'Manual on Financial and Banking Statistics‘ on the recommendation of the Steering Committee set up by the Ministry of Statistics and Programme Implementation, Government of India. The objective of this reference manual was to provide a methodological framework for compilation of statistical indicators, encompassing various sectors, including the FOF accounts apart from monetary statistics, banking statistics, external sector statistics and fiscal sector statistics,, published by the RBI. Subsequently, the High-Level Committee on the Estimation of Savings and Investment (HLC) (Chairman: Dr. C. Rangarajan), 2009 also made some recommendations for improving the compilation of the FOF accounts. Some of the recommendations of the HLC have already been incorporated in the compilation methodology of FOF in subsequent years.

6. Internationally, the standards for preparing macroeconomic statistics including the detailed flow of funds accounts have evolved over the years with the System of National Accounts (SNA)3 being the internationally agreed standard set of recommendations on how to compile measures of economic activity. The SNA describes a coherent, consistent and integrated set of macroeconomic accounts in the context of a set of internationally agreed concepts, definitions, classifications and accounting rules. The SNA is intended for use by all countries, having been designed to accommodate the needs of countries at different stages of economic development. It also provides an overarching framework for standards in other domains of economic statistics, facilitating the integration of these statistical systems to achieve consistency with the national accounts.

7. The standards for preparing macroeconomic statistics changed significantly following the publication of the 1993 SNA, which set out the overarching conceptual framework for all macroeconomic statistics. The 1993 SNA incorporated two significant enhancements: the full integration of stocks (balance sheets) and flows, and a complete sets of accounts covering production, income, consumption, saving, investment, and financial activities for sectors of the economy as well as for the economy as a whole. The 2008 SNA4 is an update on the 1993 SNA which takes into account new developments in economic activities and analysis since 1993.

8. In the case of the FOF accounts of the Indian economy, it may be mentioned that the sectorisation and the instruments adopted as per the existing methodology partially match with the SNA‘s sectorisation and instruments. However, the Indian FOF accounts, as per the existing methodology, differ from the SNA in some aspects, namely:

  1. the (opening and closing) balance sheets are not published in the Indian FOF accounts.

  2. the Indian FOF accounts does not segregate the financial flows - period-to-period changes in the outstanding amounts of financial assets and liabilities - into transactions, revaluations, and other changes in the volume of assets (OCVA),

  3. the Indian FOF accounts presents only the consolidated tables wherein the intra-sector transactions are netted out;

  4. the sectorisation in the Indian FOF accounts does not give the disaggregation of institutional units in terms of public, private and foreign companies for the financial and non-financial corporations;

  5. In the financial corporations sector, the Indian FOF accounts do not give the disaggregation in terms of deposit-taking and non-deposit taking financial corporations.

  6. As regards financial instruments, the Indian FOF accounts do not give the break-up of 'investment‘ into debt and equity, does not capture , financial derivatives and employee stock option (ESOPs), and does not give the break-up of debt securities and loans in terms of short-term and long-term.

  7. In addition to balance sheets, data for the FOF accounts are also collected through special returns from the institutional units/regulators.

9. In this backdrop, the RBI constituted a Working Group on the compilation of Flow of Funds of the Indian Economy chaired by Shri Deepak Mohanty, Executive Director, RBI to review the methodology of compilation of the FOF accounts for the Indian economy. The Working Group reviewed the existing methodology of compilation of the Indian FOF accounts as well as international best practices including the SNA system and examined the feasibility of aligning the Indian FOF accounts with the international best practices to the extent possible at the current juncture taking into account availability of data. As mentioned earlier, this Manual incorporates the recommendations of the Working Group that are currently implementable.

2. Institutional arrangements

10. The RBI is responsible for preparing the annual FOF accounts for the Indian economy. In the RBI, the National Accounts Analysis Division (NAAD) in the Department of Economic and Policy Research (DEPR) does the compilation of the FOF accounts sourcing the data - from various Government of India agencies, namely the Central Statistics Office (CSO) in the Ministry of Statistics and Programme Implementation (MOSPI), Ministry of Corporate Affairs (MCA), Ministry of Heavy Industries and Public Enterprises (MHIPE), from various financial institutions/market regulators, viz., IRDAI, NABARD, NHB, PFRDA, SEBI, SIDBI, other Departments in RBI, namely DBR, DGBA, DSIM, DNBR, FIDD, DCBR and various regional offices, other Divisions in DEPR [which include the Division of Financial Markets (DFM), Division of International Trade and Finance (DITF), Division of Money and Credit (DMC), and Fiscal Analysis Division (FAD)] as well as from individual corporations, such as State Electricity Boards/Departments/Power Generation, Transmission and Distribution Companies, etc.

11. The methodology for the compilation of the FOF is the responsibility of the RBI which is, however finalised in close coordination with the Central Statistics Office (CSO) in the Ministry of Statistics and Programme Implementation (MOSPI).

4. Institutional Units

12. The new methodology of compilation of FOF accounts for the Indian economy would align the grouping of institutional units into various sectors along the lines of SNA 2008 to the extent feasible (see Chart below).

13. However, coverage of the institutional units would depend upon the availability of data. It may be mentioned that, at the current juncture, consolidated data pertaining to Non-Profit Institutions (NPIs) serving households are not readily available and hence the financial flows of these institutions are not separately reported.

5. Institutional Sectors

14. As recommended by the Working Group on Compilation of the FOF Accounts of the Indian Economy, 2015, the list of institutional sectors, sub-sectors and the list of institutional units that would be covered in the FoF accounts of the Indian economy are given in the Table 1 below. This sectorisation is generally in line with the 2008 SNA.

Table 1: Sectorisation of the Indian FOF Accounts
S.No. Sector (SNA Code) Sub-sector List of Institutional Units
1 Non – Financial Corporations  
    Non-Government Non- Financial Companies 1. Public Limited Companies
2. Private Limited Companies
3. Power sector utilities (and State Electricity Boards)
    Government Departmental and Non-Departmental Commercial Undertakings 1. Central and State Public Sector Enterprises
2. Port Trusts
    Cooperative non-credit societies 1. Cooperative non-credit societies
2 Financial Corporations  
    Central Bank Reserve Bank of India
    Deposit-taking corporations, except the central bank 1. Scheduled Commercial Banks
a. State Bank of India (SBI) and associates
b. Public Sector Banks (including IDBI Bank)
c. Regional Rural Banks
d. Old Private Sector Banks
e. New Private Sector Banks
f. Foreign Banks
2. Primary/Urban Cooperative Banks (UCBs)
3. State Cooperative Banks (StCBs)
4. District Central Cooperative Banks (DCCBs)
5. Deposit accepting primary (agricultural and non- agricultural) credit and non- credit societies
6. SCARDBs and PCARDBs
7. Deposit-taking NBFCs (NBFC-D)
8. Deposit-taking HFCs
9. Local Area Banks
    Mutual Funds 1. Mutual Funds
a. Money Market Funds (MMF)
b. Non-MMFs
    Other Financial intermediaries, except insurance corporations and pension funds 1. Non-Deposit taking NBFCs (systemically important and others)
2. Non-Deposit taking HFCs
3. Non-Deposit taking primary cooperative credit societies
4. All India Financial Institution (NABARD, EXIM Bank)
5. State Financial Corporations (SFCs)
6. State Industrial Development Corporations (SIDCs)
7. State Industrial Infrastructure Development Corporations (SIIDCs)
8. IFCI, IDFC, REC, PFC, IFCI Venture Capital Funds Limited, Indian Renewable Energy Development Agency Limited (IREDA), India Infrastructure Finance Company Limited (IIFC), Indian Railway Finance Corporation (IRFC), NCDC, NABARD, NHB, EXIM Bank, SIDBI, Industrial Investment Bank of India, SBI DFHI and SBI Capital
    Financial Auxiliaries Insurance brokers, loan brokers, share brokers, floatation corporation, Asset Management Companies, Securities Trading and Clearing Corporation (STCL), Companies of Pension Funds, Companies engaged in Foreign Exchange activities depending on data availability
    Captive Financial Institutions and Money Lenders Holding Companies, Special Purpose Vehicles (SPVs), Companies in Financial Activities, Money lenders, Pawn brokers, etc. depending on data availability
    Insurance Corporations 1. Life Insurance Companies
a. Public
b. Private
2. General Insurance Companies (including Health Insurance and Reinsurance companies)
a. Public
b. Private
    Pension Funds 1. Provident Funds (PF)
i. Public Provident Fund
ii. Employees Provident Fund Organisation (EPF)
iii. Non-Government Provident Funds1
iv. Local Authorities P.F.
2. Pension Funds
a. New Pension Scheme
b. Non-Government
3 General Government  
    Central Government including autonomous bodies  
    State Governments All the State Governments and Union Territories
    Local Authorities Municipalities, Corporations, Rural Bodies, Local Institution
4 Households and NPISH Households and NPISH
5 Rest of the World Non-Residents transactions with residents
1. The Central Government and State Governments‘ Employees Provident Funds are included in the Central and State Government FOF accounts, respectively.

6. Financial Assets and Liabilities (Instruments)

15. As recommended by the Working Group on Compilation of the Flow of Funds Accounts of the Indian Economy, 2014, the following financial instruments would be covered in the FOF accounts for the Indian economy to the extent feasible depending on the availability of data (Table 2).

Table 2: List of Financial Instruments covered in the FOF Accounts for the Indian Economy
Monetary Gold and Special Drawing Rights (SDRs)
Monetary Gold
SDRs
Currency and Deposits
Currency
Deposits
Debt securities
Loans
Equity and investment fund shares
Insurance, pension and standardised guarantee schemes
Other accounts receivable/payable

7. Sources of data

16. The source agencies/source documents for each of the institutional sectors in the FOF accounts are given in the Table below.

Table 3: Sources of data5
Sector and sub-sector (SNA Code) Source agency Source document (s)
Non-Financial Corporations
Public Limited Companies RBI (DSIM), SEBI and Ministry of Corporate Affairs (MCA)  
Private Limited Companies RBI (DSIM), SEBI and MCA  
Government Non- departmental commercial undertakings (NDCUs) Ministry of Heavy Industries and Public Enterprises Survey of Public Sector Enterprises (PSEs)
Power Utility Companies Respective power utility company  
Cooperative non-credit societies NABARD Statistical Statements relating to the Cooperative Movement in India, which is available with a time lag.
Financial Corporations
Central Bank RBI (DGBA, various regional offices) 1. RBI Statement of Affairs
2. Real Time Handbook of Statistics on the Indian Economy (www.dbie.rbi.org.in)
Deposit-taking corporations, except the central bank    
Commercial Banks RBI (DSIM) FIDD Form X returns (from DSIM)
Statistical Tables relating to Banks in India (from DSIM)
Section 42 returns (from DSIM)
Form X returns for RRBs (from RPCD)
Local Area Banks RBI (DCBR)  
Cooperative Banks    
State Cooperative Banks NABARD/RBI (DCBR) 1. FIDD Form IX return
2. Statistical Tables relating to Banks in India
3. Statistical Statements relating to the Cooperative Movement in India, which is available with a time lag.
District Central Cooperative Banks NABARD/ (DCBR) RBI
Urban Cooperative Banks DCBR, RBI  
Deposit-taking Non-Banking Finance Companies (NBFC- D) RBI (DNBR and DSIM)  
Deposit-taking Housing Finance Companies (HFC-D) National Housing Bank (NHB)  
Mutual Funds Securities and Exchange Board of India (SEBI)  
Other financial intermediaries, except insurance corporations and pension funds Annual Reports of the respective institutions  
Non-deposit-taking Non-Banking Financial Companies RBI (DNBR)  
Non-deposit-taking Housing Finance Companies NHB  
Insurance Corporations IRDAI IRDAI Annual Report, LIC Annual Report
Pension Funds   Respective Provident and Pension Funds
General Government  
Central Government   1. Economic and Functional Classification of the Union Budget,
2. Union Budget documents,
3. Union Finance Accounts (UFA) (http://cga.nic.in/)
4. Combined Finance and Revenue Accounts (CFRA) (http://cag.gov.in/)
State Governments RBI (FAD) 1. State Budgets
2. Combined Finance and Revenue Accounts (CFRA)
Local Authorities MOSPI (CSO)  
Households and Non-Profit Institutions serving Households  
Rest of the World RBI (DITF) Balance of Payments Statistics (BoP)

8. Balancing the accounts

a) Consistency between financial and non financial accounts

17. Generally, statistical discrepancies between the financial and non-financial accounts are observed. The reasons for the discrepancies could be on account of the following, inter alia: different scheme of classification of units to a sector (or a sub-sector), use of different sources in compiling the financial and non-financial accounts, difficulties in identifying creditors of some financial assets, and revision of accounts. The FOF accounts attempt to minimise the discrepancy between the financial and non-financial accounts to the extent possible.

b) Stock/flow consistency

18. The 2008SNA/ ESA95 framework presents a full set of accounts so that for any financial instrument and sector, changes between opening and closing balance sheets are divided into financial transactions and "other flows". The "other flows" cover changes in volume not resulting from a transaction between units and changes resulting only from a change in prices of financial assets and liabilities (including change in exchange rate), that is valuation change.

19. As a general rule, the method used for estimating financial transactions is the "changes in stocks adjusted by information on other flows". However, direct data on transactions is used when they are available. It is notably the case for net issuance of securities where market information exists and is reliable, at least for securities traded on an organised market. Purchases and sales of Mutual Fund Shares are frequently available. Balance of Payments transactions are also an example of "direct information".

20. In the Indian context, the financial flows in the FOF accounts of various sectors (except the ROW sector) are and some parts of the Central and State Government Sectors, obtained as the difference between the outstanding financial assets/liabilities in two consecutive end-March positions. At present, the financial flows obtained by differencing the two outstanding positions are not bifurcated into transactions, revaluations (such as capital gains and losses and changes owing to movements in the exchange rate) and other changes in volume account (OCVA) such as write-off of loans.

c) Consistency checks/Control Totals

21. In order to maintain consistency in the financial flows across sectors in cases when aggregate numbers are available from two different sources, the data that is firmer is chosen. Furthermore, some aggregate flows, which are available for certain sectors, are used as control totals, so as to ensure consistency with the sum of the constituents, on which data are separately available (Table 3).

Table 4: Consistency checks and control totals
S. No Sector/sub-sectors Source data and agency Control Totals
Sources of Funds (Liabilities) Uses of Funds (Financial assets)
1 Non-Financial Corporations (PCB sector)  
  Non-Government Non-Financial Companies Sample data from DSIM (RBI) Data on Global Paid-up Capital (PUC) of all companies available with MCA is used as the control total for the total flow in equity.6
  Cooperative non-credit societies NABARD Statistical Statements relating to the Cooperative Movement in India, which is available with a time lag.
  Power sector companies Individual companies Some items available in the report on 'Performance of State Power Utilities‘ are: Equity, State Government Loans, Loans from FIs/Banks/Bonds, Other Loans, Grants towards capital assets, Consumer contribution, Creditors for purchase of power. Not available
  Non- Departmental Commercial Undertakings (NDCUs) CPSE survey available on the website of the Department of Public Enterprises (DPE) The CPSE Survey, includes certain public sector financial institutions (registered as NBFCs). The total liabilities and assets of these financial institutions are deducted to arrive at the total sources or uses of funds for the NDCUs.
2 Financial Corporations    
a) Banking sector      
  RBI DGBA (RBI) Total liabilities as well as the instrument-wise break up are available as at end-March for the RBI Total assets as well as the instrument- wise break up are available as at end- March for the RBI
  Commercial Banks (excluding RRBs) Returns from Scheduled commercial banks (e.g., Form X returns )/ Statistical Tables relating to Banks in India / Section 42 returns from DSIM (RBI)    
  RRBs Form X returns / NABARD Total liabilities of RRBs as per FIDD data Total assets of RRBs as per FIDD data
  Cooperative banks      
  StCBs DCBR (RBI) / NABARD Total Liabilities as per DCBR‘s Form IX returns data Total Assets as per DCBR‘s Form IX returns data
  DCCBs NABARD Total Liabilities as per DCBR‘s Form IX returns data Total Assets as per DCBR‘s Form IX returns data
  UCBs NABARD Total Liabilities as per DCBR data Total Assets as per DCBR data
  State Finance Corporations (SFCs) SIDBI and respective SFCs Total liabilities of all SFCs as at end-March Total assets of SIDCs as at end- March
  State Industrial Development Corporations (SIDC) Respective SIDCs Total liabilities of all SIDCs as at end-March Total assets of SFCs as at end- March
  State Industrial Infrastructure Development Corporations (SIIDC) Respective SIIDCs    
b) OFI      
  NBFCs DNBR and DSIM (RBI) Total Liabilities as per RBI data Total Assets as per RBI data
  Insurance Companies IRDAI Total liabilities of insurance companies as at end-March Total assets of insurance companies as at end-March
  Mutual Funds SEBI SEBI publishes data on Mutual Funds‘ total resource mobilisation (gross mobilisation), redemption and net inflows  
  Non-Government Provident Funds CSO/Individual PFs Data provided by the CSO as well as the individual PFs are used as control totals
  HFCs NHB Total as given by NHB Total as given by NHB
4 Government      
  Central Government Union Budget, Finance Accounts, CFRA Economic and Functional Classification of Union Budget (EFC), the Combined Finance and Revenue Accounts (CFRA) of the CAG.
  State Governments FAD (DEPR, RBI)
  Local Authorities CSO The FOF accounts of this sector would be derived, to the extent possible, from the flows observed in the other sectors. *
5 Household and NPISHs Various sources Not available Not available
6 ROW DITF (DEPR, RBI) Total sources and uses of funds as well as instrument-wise totals are derived from the BOP Statistics
* The availability of further data is being examined.

B. Compilation Guide

1. Total Economy

22. India‘s FOF accounts categorise the domestic economy into four major sectors, namely, financial corporations, non-financial corporations, the general government, and the 'households and NPISH‘ as is the current practice globally.

23. The FOF accounts will publish both the non-Consolidated Tables wherein the intra-sector transactions are reported and the Consolidated Tables wherein the intra-sector transactions are netted out.

1.1 Non-Financial Corporations

24. This sector comprise of the following institutional units:

1. Non-Government Non-Financial Companies

  1. Public and Private Limited Companies

  2. Private Power Sector Companies

  3. Private Port Trusts

  4. Cooperative Non-Credit Societies

2. Government departmental and non-departmental Commercial Undertakings

  1. Government departmental undertakings

  2. Central and State Public sector enterprises

  3. Port Trusts

  4. State Electricity Boards/State Power Utilities

1.1.1. Non-Government non-financial companies

25. This sub- sector comprises all public and private limited companies registered in India under the Indian Companies Act, 2013 (and the earlier Indian Companies Act, 1956) and branches of foreign companies operating in India. Studies on 'Finances of public / private limited companies‘ published periodically in the RBI Bulletin, form the basic source for compilation of the accounts of this sub-sector.

26. These studies cover a sample of operating non-government non-financial public and private limited companies.7 The company finance studies of the Bank cover only a small sample of medium and large public limited companies and private limited companies whose audited Annual Reports and Accounts are received in the Reserve Bank of India [DSIM],

27. The number of companies covered in the Bank‘s studies is revised/enlarged on a quinquennial basis. As the studies include only limited number of companies, the data presented therein are adjusted for the under coverage on the basis of the indicator available for populations of the public and the private limited companies. Total paid-up capital of these companies as on 31st March is used to get global estimates for public and private limited companies. Further, in order to arrive at global estimates, the sample data of each and every item (both financial and nonfinancial) are blown-up using the blow-up factor arrived as the ratio of paid-up capital of the global population to paid-up capital of the sample. The underlying assumption in such blowing–up is that the relationship between the characteristics (estimated) of the population and those of the sample companies is the same as that of the paid-up capital of sample companies to the paid-up capital of the population.

28. In order to ensure accuracy and consistency, after estimating the blown-up numbers for a particular instrument, the number is compared with corresponding flows in other sectors (such as the Commercial Banks sector or the ROW sector). In case of wide variations, the Banking sector or the BOP sector number is used as they are considered to be more firm data.

29. The FOF accounts of the non-government non-financial public and private limited companies, as stated above, are based on the blown-up data of the sample companies. As all the necessary details are not available from these studies, these are supplemented with the information collected from the accounts of other sectors as well as from records of the Bank (DSIM).8

Liabilities

Deposits

30. Deposits accepted by the companies from public are shown under 'Long term borrowing‘ are 'Short-Term borrowings‘. Further, the deposits are segregated under secured and unsecured. For the purpose of the FOF accounts, these public deposits are treated as deposits raised from the household sector.

Debt Securities

31. The company finances information provides data on money raised through bonds/debentures. In the absence of ownership details of these securities, the investments made by other sectors (mainly the financial corporations) in the debt securities of non-financial corporations are used.

Loans (Borrowings)

32. Long-term borrowings include term loans (including loans from banks), deposits, loan and advances from selected parties, long-term securities of finance lease obligation and from 'others‘. In addition, information relating to short-term borrowings including that from banks is available.

Equity (Paid-up capital)

33. The studies on company finances provide information on shareholders‘ funds which include (i) share capital and (ii) reserves and surplus.

34. The total share capital is segregated according to its ownership on the basis of the sectoral accounts which report their investments in the shares of nongovernment non-financial companies. The paid–up capital held by the household sector is obtained as a residual, i.e., by deducting the investments of all identifiable sectors from the total share capital of the companies.

Trade payables and other current liabilities

35. Trade payables include companies‘ purchases on deferred payment basis from other non-government companies, government undertakings, partnership firms and proprietorship firms and other business households. However, these ownership details are not available in the Company Finance studies. A similar item, 'trade receivables‘ appears under assets which includes the sale of goods on deferred payments basis to various parties such as, the other non-government companies, government undertakings, partnership and proprietary concerns, the details of which are also not available. In the absence of ownership particulars, the intra-corporate trade transactions are excluded and the residual is taken as the amount received from/paid to the household sector. The 'other current liabilities‘ and classified as 'items not elsewhere classified‘ under the instruments are 'sector unidentified' for the sectoral allocation.

Reserves and Surplus

36. Reserves and surplus include different types of reserves, such as, capital reserve, investment allowance reserve, sinking funds and other reserves. Capital reserve includes profit/loss on sale of fixed assets and/ or investments, profits realised on purchase of company‘s own debentures, profit on sale of forfeited shares, capital redemption reserves, revaluation reserves (fixed assets), and premium on shares. Hence, increase in capital reserve does not form part of the saving of the companies. Increase in reserves (other than capital reserve) forms the saving of the companies which is a non-financial transaction.

Provisions

37. Provisions include provision for taxation, other non-current provisions and other current provisions. The companies also show 'advance income tax paid‘ under current assets. Increase in tax provision net of tax advance over the previous year‘s closing balance forms part of the saving of the companies, whereas other current provisions relate to provisions for contingencies and bonus to staff. They are not included under saving but are shown as other non-financial capital receipts.

Financial Assets

Currency and deposit

Cash and cash equivalents

38. Cash on hand are shown against this sub-head. As mentioned earlier for other sectors, cash on hand is split into bank notes and government notes.

Deposit

39. Deposits with commercial banks, shown as balances with Banks in the Balance sheets, cover the fixed, current and other deposit accounts.

Investments (Debt and equity securities)

40. Companies‘ investments are classified into non-current and current investments. The former includes investment in equity instruments/shares, government or trust securities, debentures/bonds, mutual funds and others. In the absence of any details, the last category 'other investments‘ is shown as sector unidentified.

Loans and advances

41. Both long-term and short-term loans and advances are shown under this head. Loans to others are shown as 'sector unidentified‘ as no details are available. Long-term loans and advances include capital advance, security deposits, loans to related parties and 'others‘.

Other non current assets

42. These assets which appear under a separate head are classified as other items not elsewhere classified under financial flows.

Non-Financial Assets

Net fixed assets

43. The increase in net fixed assets constitutes the net fixed capital formation of these companies, which is a non-financial transaction. The company finances data provides information relating to gross fixed assets, which include tangible assets (such as land, buildings, plant and machinery, furniture, fixtures and office equipments and others), capital work in progress and intangible assets, but these details are not available on net basis.

Inventories

44. Inventories include stocks of raw materials and components, stores and spares used by the company for the maintenance of its fixed assets, stocks of finished goods, work-in progress and other inventories. The annual variation in stock is a non-financial transaction.

2.2.2 Power Sector Companies and State Electricity Boards

45. Most of the State Governments have un-bundled the erstwhile State Electricity Boards (SEBs) into one or more Power Generation, Transmission and Distribution Companies. The necessary data for these companies and the existing SEBs are obtained through special returns from these companies and supplemented with their annual reports. The special returns provide the sectoral details of all the financial assets and liabilities.

Financial Liabilities

Deposits

46. The Annual Reports of the State Power Utilities/electricity boards provide details of deposits (security deposits from contractors, consumers‘ and meter security deposits, retention money and other miscellaneous deposits) received by these firms. As the ownership details of fixed deposits and consumers‘ security deposits, received by these utilities/boards are not available, the amounts are shown as of 'Household Sector‘ for sectoral presentation.

Debt securities (bonds and debentures)

47. The Annual Reports of the State Power Utilities/electricity boards provide details of their money raised through bonds and debentures. The category-wise particulars of bonds and debentures, issued by them, are not available in their reports. These details are worked out from the investing sectors‘ accounts. The bonds and debentures are subscribed mainly by the commercial banks, LIC, financial corporations, non-government provident fund authorities and state governments. The state governments‘ investment is derived by deducting other sectors‘ investment in the bonds from the total amount of bonds issued.

Loans (Borrowings)

48. The Annual Reports of the State Power Utilities/electricity boards provide details of their borrowings from Banks and Others. The borrowings from others include that from state governments, central governments, financial corporations, the LIC and other insurance companies, etc. Other details are worked out from the lending sectors‘ accounts.

Other accounts payable

Trade payables

49. Trade payables include amount due to micro and small enterprises and others including amount payable for purchase of power. The trade credit includes creditors on open account, sundry creditors, dues payable to contractors and suppliers of stores, etc. However, the particulars of the parties, to whom the amounts are payable, are not available. Under assets side, similar items, viz., sundry debtors for electricity supplied, debtors for amount paid on account of contracts in course of completion, debtors for sale and hire purchase, advances against supply of materials, sundry debtors for temporary service connection form trade receivable. The sectoral details of the trade receivables are available which is used to allocate trade payable (net of trade receivables) to government, private corporate sector and households.

Other liabilities (current and non-current)

50. The annual reports and accounts of the State Electricity Boards/State power utilities (SEBs/SPUs) present a variety of items under other current liabilities. These items which have the bearing of a liability to others, such as interest, bills payable, etc., are considered as financial transactions. The employees‘ provident fund is also shown under other current liabilities by many of the SEBs/SPUs while a few others show this under the head of provisions. For the compilation of accounts, the provident fund amount is excluded from the liabilities. To the extent possible, the amount of other liabilities (financial part) is shown against different sectors.

Reserves, provisions and capital transfers

51. All types of reserves and other funds are non-financial items and represent accumulated saving of the SEBs/SPUs. The increase in taxation provision net of advance tax payments, is the saving of the SEBs/SPUs. Capital transfers comprise revenue subsidy/grant from state governments.

Financial Assets

Currency and deposits

Currency (Cash on hand)

52. Data on cash on hand are available directly in the basic source.

Deposits

53. Balances at banks include the deposits in current and fixed deposit accounts with commercial banks and co-operative banks.

Loans

54. This item comprises loans and advances to employees, contractors, suppliers and others. Loans to employees are classified as loans to the household sector.

Investments (Debt and Equity)

55. The annual reports provide the details of investments of the SEBs/SPUs.

Other accounts receivable (Other assets)

56. Other assets include interest accrued, sundry receivable, receivables from state government and other agencies and certain non-financial transactions. Financial items of other assets are identified under different sectors depending upon the availability of details in the annual reports. The non-financial items of other assets are shown as other capital transfer payments under non-financial flow accounts.

Non-financial items

57. Net fixed assets, inventories, capital transfers, viz. grants, subsidies, advance of tax payment and other assets (non-financial) appear under non-financial flows. The items such as deferred revenue expenditure and net revenue and appropriation account, appearing under other assets, are deducted from reserve funds to derive the saving of the SEBs/SPUs. Net fixed assets and inventories together form the physical assets and the additions made therein during the year represent the capital formation (net) of the electricity boards/SPUs.

2.2.3 Co-operative non-credit societies

58. The co-operative non-credit societies comprise primary marketing and processing societies, co-operative sugar factories, cotton ginning and pressing societies, milk supply unions and societies, fisheries societies, farming societies, irrigation societies, consumers‘ co-operative stores, housing societies, weavers‘ societies, spinning mills, dairy cooperative societies, poultry union and societies and multiunit societies.

59. The Statistical Statements on the Cooperative Movement in India, which is the primary source for these Cooperatives, is published with a considerable lag. Therefore, the FOF accounts for these societies for the years under review are estimated by applying the same growth rate as observed under the relevant financial instruments of the DCCBs as per the Form IX return of the DCBR, RBI. These estimates are revised as and when the Statistical Statements of the particular year are released by NABARD.

2.2.4 Port Trusts

60. The FOF accounts of this sector are prepared by obtaining special returns from the major port trusts, both in the public and private sector. The special returns provides the sectoral details of all the financial assets and liabilities.

Liabilities

61. The major sources of finance of public sector port trusts are in the form of borrowings and other financial liabilities (for example, sundry creditors) which arise in the course of business. Their borrowings are mainly from the central government.

62. Along with the reserves and other funds, the balance sheet of Port Trusts presents provident fund contributions of the employees. Since the nongovernment provident funds(other than state provident funds) is a separate subsector, the provident fund data of the port trusts are excluded from the liabilities side as well as from the assets side and shown in the provident fund sub-sector.

63. The change in reserves and other funds including provisions for depreciation, form the gross saving of the trusts. But, 'net deficit‘, and 'uncovered revenue deficit‘ have been presented under 'other assets‘, which generally indicate accumulated losses. Therefore, these two items are adjusted before deriving the net/gross saving of the port trusts.

Assets

64. Under the assets, investments made out of provident funds contributions are shown separately. As stated earlier, this investment account is separated from the assets. In practice, provident fund contributions shown in the liabilities do not match with the investments from the fund given under assets. The difference between these two sets of figures is adjusted under the item 'bank balances‘.

65. Investments made out of general fund and other funds (other than provident fund account) are in the form of securities of financial institutions, private corporate sector and government, and these details are available in the annual reports. The data on cash in hand and their deposits (current and fixed) with banks and other financial assets (such as sundry debtors and accrued interest) are the remaining financial assets of the port trusts. The deposits are treated as deposits with commercial banks. In the case of other financial assets which include interest accrued and such other items, no sectoral details are available.

66. Plant and machinery, premises, furniture and other fixed assets are presented on gross basis inclusive of depreciation in the annual reports. Therefore, rise in these assets would mean gross fixed capital formation of the port trusts. Similarly, the increase in the stores and raw materials maintained by them would be change in inventories. As sated earlier, the 'net deficit‘ and 'uncovered revenue deficit‘ are covered under 'other assets‘ and these are deducted from reserves to derive the saving of the port trusts.

2.2.5 Government Non-departmental non-financial undertakings

67. This sub-sector covers all Government non-departmental non-financial companies owned either singly or jointly by central, state or local governments. In the case of the companies owned by the central government, the data on their assets and liabilities are available in the annual publication titled Public Enterprises Survey (PES) which is published by the Department of Public Enterprises (DPE), Ministry of Heavy Industries and Public Enterprises (MHIPE), Government of India on its website.

68. This publication covers enterprises under construction, and running enterprises, which are promotional, financial and non-financial companies by their activity. However, the financial companies are not included in this sub-sector as they are covered under the 'Financial Corporations‘ sector.

69. The State Public Sector Enterprises are (i) companies under section 617 of companies Act, 1956, (ii) Statutory companies established under the act of state legislature (iii) any other companies not categorised by the state government.

70. The Department of Public Enterprises published the first National survey of State level Public Enterprises (2006-07) in August 2009 and the second survey for 2007-08 in 2012. Due to the time lag in availability of data from the source, the data on SLPEs are compiled from the CAG reports or data disseminated by state governments. The methodology for central and state government companies is given below.

(i) Central and State Government Companies

Liabilities

Loans (Borrowings)

71. The sectoral borrowing in respect of CPSEs, are drawn from a subsidiary statement on details of short-term and long-term borrowings from (i) central government, (ii) state governments, (iii) holding companies, (iv) banks, (v) foreign parties and (vi) others.

Equity (Paid-up capital)

72. In the case of CPSEs, Statement I of the PES, Vol. I, provides the balance sheet data for running enterprises and companies under construction. The ownership details of the paid-up capital are available in subsidiary statement of the PES, Vol. I, against the following heads: (i) central government, (ii) state governments, (iii) holding companies, (iv) financial institutions (Indian), (v) foreign parties, (vi) employees and (vii) others (Indian). The data on financial institutions are not given separately for banking and other financial institutions. Therefore, the equity holding of banks in NDCUs as per the FOF accounts of Scheduled Commercial Banks are shown here. The remaining are shown under non-banking financial institutions.

Current and non-current liabilities (other than borrowings) and provisions

73. The current and non-current liabilities (other than short-term borrowings) are further divided into (i) trade payables and, (ii) other long term liabilities, (iii) provisions (long-term and short-term) and deferred tax liability. The taxation provision and other short and long term provisions included under 'provisions‘ appear under non-financial flows.

Reserves and Surplus

74. The item 'Reserves and Surplus‘ includes general and other free reserves, specific reserves and balance from profit and loss account. The changes in these funds represent the saving of the companies. While, change in tax provision net of advance tax payments is added, changes in 'deficit‘ and 'deferred revenue expenditure‘ appearing under assets side of central government companies are deducted from change in reserves.

Financial Assets

Cash and bank balances

75. Cash in hand is the currency held by the companies while bank balances, including fixed deposits with commercial banks, are shown as deposits with commercial banks. In the case of CPSEs, the break-up of cash in hand and balances with commercial banks is not available. The cash

Loans (Loans and advances, Trade receivables and other assets)

76. Details of loans and advance extended by the CPSEs are not available. In the absence of any loan details, the total amount under this head, other than loans to subsidiary and holding companies, is shown under unclassified sector. The amount under trade receivables is shown as trade debt and is not allocated to any identifiable sector for want of details. Other assets are shown under 'other items not classified elsewhere‘ for instrument-wise classification and 'unclassified‘ for sectoral presentation.

77. The PSE Survey gives data on inventories and gross fixed assets i.e., fixed assets and inventories inclusive of depreciation. Fixed assets are also shown net of depreciation. The total fixed assets of CPSEs also capital work-in progress, which includes capital advances to suppliers/contractors and intangible assets under development. The PSE Survey provides data on 'deficit‘ (accumulated) and 'deferred revenue expenditure‘ in the case of CPSEs and is shown as other capital transfer payments. Changes in these items are deducted from the change in reserves and surplus to arrive at the net saving of these companies.

1.2 Financial Corporations

78. The Financial Corporations sector would now comprise the institutional units as given in Table 1.

1.2.1 Central Bank – the Reserve Bank of India

79. The Statement of Affairs of the RBI prepared by DGBA and also published in the RBI‘s Handbook of Statistics on the Indian Economy gives the assets and liabilities of the RBI as on March 31st. This data forms the basic source for the compilation of the FOF accounts of the RBI.

Liabilities

Currency and deposits

Currency (Notes issued)

80. This item includes all notes issued by the Government of India up to April 1935 and by the RBI thereafter (referred to as bank notes). One rupee notes and coins issued by the Government of India since July 1940 (referred to as government notes) are considered as rupee coins. These coins are the liabilities of the Government of India and those held by the Reserve Bank are the assets of the Bank. These do not, therefore, form part of 'notes in circulation‘ presented in RBI accounts. Thus, 'notes issued‘ comprise bank notes (i) held in Banking Department of the RBI, and (ii) in circulation (i.e. bank notes outside the RBI).

81. Bank notes and rupee coins in circulation are held by different institutions/subsectors, such as banks, co-operative societies, financial corporations, insurance companies, non-government and government companies, government treasuries, the railways, the posts and telegraphs in the form of petty cash, and households who maintain the cash balances with them for day to day transactions and also as a part of their saving. As there is no single source which gives data on cash holdings of these different institutions/sub-sectors, their cash on hand, as reported in their accounts, is made use of to derive the sectoral distribution of notes and rupee coins in circulation.

82. However, as the cash held by different institutions/sub-sectors includes both the bank notes and the government notes, the break-up of these notes into bank notes and others for each sub-sector is made by assuming that their ratio to one another is the same as indicated in the data on total 'notes in circulation‘ and total 'circulation of rupee coins and small coins as on 31st March of the respective year. The sectoral distribution of bank notes worked out on the above basis is shown under this item. Data on cash holdings of each institution/sub-sector, except the household sector, are obtained from their respective balance sheets/annual reports. Cash holdings of the Household Sector are, however, derived by deducting cash held by different identifiable institutions/sub-sectors from the total currency in circulation excluding government notes.

Deposits

83. Deposits with the Bank are shown against the following heads. (a) Government: (i) Central Government, (ii) State Governments, (b) Banks: (i) Scheduled Commercial Banks, (ii) Scheduled State Co-operative Banks, (iii) Other Banks, and (c) Others.

84. The category 'other banks‘ includes deposits of (i) non-scheduled commercial banks, and (ii) central and primary co-operative banks which have been permitted to open accounts with the Bank. In the absence of the availability of separate details for the non-scheduled commercial banks and the co-operative banks, the deposits of the category 'other banks‘, are classified under commercial banks.

85. The last category – 'others‘ – include (i) rupee deposits from Foreign Central Banks and Foreign Financial Institutions, (ii) Deposits from Indian Financial Institutions, (iii) Deposits placed by Mutual Funds, (iv) Accumulated Retirement Benefits – (a) Provident Fund and (b) Gratuity and Superannuation Fund, and (v) Miscellaneous deposits, viz., balances of Clearing Corporation of India Ltd, Primary Dealers, Employee credit societies, etc., and sundry deposits. These deposits are respectively categorized under the sectors ROW, Other Financial Intermediaries, Mutual Funds, and Pension/Provident Funds. After estimating the sectoral figures, the deposits held under Account No. I of IMF with the Bank is deducted from the deposits estimated against the Rest of the World sector and shown separately as loans from the IMF. This modification is made in the accounts of the RBI sub-sector because the above transactions in the Rest of the World sector‘s accounts are shown as loans to the official sector (RBI). Thus, the deposits with the Bank are classified into the various FOF sectors.

Equity and investment fund shares

Equity

Unlisted shares (Paid-up capital)

86. The paid-up capital of the Bank, which is entirely contributed by the Government of India, has remained constant at ₹5 crore since 1948. As there is no change in the amount so far, the flow on this account is nil.

Other accounts receivable/payable

Bills Payable

87. 'Bills payable‘ include (a) outstanding Demand Drafts (DD) issued between offices of the RBI (b) outstanding payment orders (PO) issued by the RBI for local payments, and (c) outstanding balance in the remittance clearance account, representing the remittances issued as per the erstwhile Remittance Facility Scheme – between (i) treasury agencies, (ii) treasury agencies and banks, (iii) treasury agencies and the RBI, (iv) agency banks and the RBI, etc. A special return as on 31st March from the regional offices of the RBI provides the particulars of bills payable into (1) outstanding balances DDs issued between offices of the RBI and (2) outstanding balances of (a) POs issued by the RBI offices for local payment and outstanding remittances in the Remittance Clearance Account. The amount of 'bills payable, as given in the Statement of Affairs, is allocated to different sectors on the basis of the sectoral pattern derived from the special return. It may be mentioned here that while intra-RBI transactions are shown against category (1), the sectoral details are shown under category (2). While both the categories would be shown in the non-Consolidated Table, only category 2 would be reported in the Consolidated Table.

Other liabilities

88. Internal reserves and provisions of the RBI are major components of 'other liabilities‘. While Contingency Reserve (CR) and Asset Development Reserve (ADR) form the RBI‘s internal reserves, having been provided as normally provided by banks, the remaining components of 'Other Liabilities‘, such as, Currency and Gold Revaluation Account (CGRA), Investment Revaluation Account (IRA) and Exchange Equalisation Account (EEA) and provision for outstanding expenses, are in the nature of provisions as they represent unrealised gains/losses. The remaining components of 'other liabilities‘ include surplus transferable to the Government of India and miscellaneous.

89. The CR and the ADR reflected in 'Other Liabilities‘ are in addition to the 'Reserve Fund‘ held by the RBI as a distinct balance sheet head. The Contingency Reserve (CR) represents the amount set aside on a year-to-year basis for meeting unexpected and unforeseen contingencies, including depreciation in the value of securities, and risks arising out of monetary/exchange rate policy operations. In order to meet the needs of internal capital expenditure and make investments in subsidiaries and associate institutions, a further sum is provided and credited to the ADR, which was created in 1997-98.

90. Unrealised gains/losses on valuation of Foreign Currency Assets (FCA) and gold due to movements in the exchange rates and/or price of gold are not taken to the Profit and Loss Account but instead booked under the CGRA. Unlike the CR, which is created by apportioning realised gains, the CGRA is not a reserve account as it represents the accumulated net balance of unrealized gains and losses arising out of valuation of FCA and gold. As CGRA balances mirror the changes in prices of gold and in exchange rate, its balance varies with the size of asset base and volatility in the exchange rate and price of gold. The RBI values foreign dated securities at market prices prevailing on the last business day of each month and the appreciation/depreciation arising there from is transferred to the IRA. The unrealised gains/losses arising from such periodic revaluation are adjusted against the balance in IRA. The balance in the EEA represents provision made for MTM losses on forward commitments mainly arising out of intervention operations.

91. The 'Miscellaneous‘ item is a residual head including sub-accounts such as balances payable on account of leave encashment, reserve for interest earned on securities earmarked for the employee funds, the value of collateral held as margin for repo transactions and medical provisions for employees.

92. For the purpose of the FOF Accounts, only 'Other Liabilities (miscellaneous)‘ would be shown. The extent of financial and non-financial nature of these transactions is, however, not known as the details of these components are not available. However, an attempt is made to exclude the amount of non-financial transactions to the maximum possible extent, by netting similar items on the assets side.

93. The item 'other assets‘ includes (a) certain financial items, such as, housing loans to employees, loans for purchase of cars and motor cycles to employees, (b) nonfinancial items such as adjusting account, charges account, suspense account, demand drafts received for realization account, agency charges account, exchange account and dead stock account. Items under (b) would also include certain financial transactions, for which no data are available. In the absence of the break-up of details of financial and non-financial transactions of the item (b) as well as of other liabilities (miscellaneous), other liabilities (miscellaneous) net of other assets (i.e. categories (b) excluding dead stock account) are shown under financial flows.

Reserve funds & other funds

94. The reserve funds and other funds of the RBI, namely, the National Industrial Credit (Long-Term Operations) [NIC (LTO)] Fund and National Housing Credit [NHC (LTO)] Fund, form non-financial items under liabilities of the Bank. The original Reserve Fund of ₹0.05 billion was created in terms of section 46 of the RBI Act as contribution from the Central Government for the currency liability of the then sovereign government taken over by the Reserve Bank. Thereafter, ₹64.95 billion was credited to this Fund out of gains on periodic revaluation of gold up to October 1990, taking it to ₹65 billion. The accumulation in the Fund has been static since then and appreciation/depreciation on account of valuation of gold and foreign currency is booked in the Currency and Gold Revaluation Account (CGRA) which is a part of the head 'Other Liabilities‘ in RBI‘s balance sheet. Change in these reserves, namely, reserve fund, NIC (LTO) Fund, NHC (LTO) Fund, CR, and ADR is taken as the saving of the Bank.

Financial Assets

Monetary gold and SDRs

Monetary gold (Gold coins & Bullion)

95. Stocks of gold held by the Bank in the vault are shown against this head. The gold purchased by the Bank, as a part of its transactions, from the International Monetary Fund (IMF) is also included here.

96. The increase in the value of gold holdings due to revaluation is not shown under the financial flow account. This particular amount due to revaluation is shown against revaluation account under 'Other Liabilities‘ and, therefore, only the increase in the value of gold due to rise in physical stock of gold is shown under financial flow accounts. It is considered as a foreign asset and shown against the 'Rest of the World‘ sector for the sectoral classification.

Currency and deposits

Currency (Rupee coins, small coins and Bank notes)

97. This item comprises the holdings of the Issue and the Banking Departments of the Bank in the form of one rupee notes/coins and small coins and also any other coins issued by the Government of India. Rupee coins and small coins are shown as the Bank‘s claim on the Government Sector, because one rupee coins/notes and small coins are shown as the currency liability of the government.

98. Bank notes held in the Banking Department of the Bank relate to the notes issued by the Bank and as such form the transactions within the Bank.

Deposits

99. Cash balances and fixed deposits with foreign central banks and other major international commercial banks (which form part of the foreign currency assets) are included under this head.

Debt securities

100. The Statement of Affairs of the Bank presents data on investments under different heads of assets viz. Government of India (GoI) rupee securities (including treasury bills), foreign securities, Shares in BIS/SWIFT, holdings in Subsidiaries/Associate Institutions (DICGC, NABARD, NHB, BRBNMPL). While the first item is shown under this category, the second item is clubbed with 'foreign assets‘ as explained below. The last two items are shown under the instrument 'equity and investment fund shares‘.

101. The details of investments are obtained from DGBA. The securities of the GoI, treasury bills and rupee securities are shown separately in the FoF accounts. The Bank‘s investments in government treasury bills include rupee treasury bills and bills purchased and discounted, while rupee securities form the Bank‘s investments in government rupee securities.

Foreign assets

102. Foreign currency assets (FCA) of the RBI include deposits with other central banks, the Bank for International Settlements (BIS), foreign commercial banks and investments in foreign treasury bills and securities. As these relate to transactions of the Bank with foreign governments/central banks and international institutions, these are classified against the 'Rest of the World‘ sector. While investment in foreign securities is shown under debt securities, cash balances and fixed deposits with foreign central banks and other major international commercial banks are shown under 'deposits‘.

Loans (Loans and Advances)

103. The Reserve Bank extends loans and advances to central and state governments, NABARD, scheduled commercial and cooperative banks, EXIM Bank and primary dealers.

104. In addition, certain items such as staff advances, which are shown under 'other assets‘ [Miscellaneous assets] are included under this head. The particulars of these assets are obtained from DGBA as well as through a special return from the regional offices of the Bank, a reference to which was made while discussing 'other liabilities‘. Loans and advances to staff are shown as loans to Households.

Equity and investment fund shares

Equity

105. The shares of BRBNMPL, DICGC, NABARD and NHB are shown under this instrument.

Other accounts receivable/payable (Other Assets)

106. 'Other Assets‘ of Banking Department comprise fixed assets (net of depreciation), gold held abroad (265.49 metric tonnes)9, accrued income (mainly comprising interest income accrued on balance sheet date on the Bank‘s domestic and foreign investments), and miscellaneous assets. Miscellaneous assets comprise mainly loans and advances to staff, amount spent on projects pending completion, the margin offered for reverse repo transactions, security deposit paid, and items in transit representing inter-office transactions (RBI General Account).

107. As explained earlier under 'other liabilities‘, the details of financial and nonfinancial items included in 'other assets‘ are received from the regional offices of the Bank. Besides the exclusion of certain financial items from other assets, the amount under dead stock account, which relates to the fixtures, furniture and premises of the Bank, is also excluded from it before netting with 'other liabilities‘. Increase in dead stock account represents the net capital formation of the Bank.

Deposit-taking Corporations, except the Central Bank

Commercial Banks

108. This sub-sector comprises

  1. State Bank of India (SBI) and its subsidiaries,

  2. Public Sector Banks (including IDBI Bank),

  3. Private Sector Banks (old and new)

  4. Regional Rural Banks (RRBs),

  5. Foreign Banks operating in India.

  6. Other Indian non-scheduled commercial banks [Local Area Banks (LAB)].

109. The financial assets and liabilities data of Scheduled Commercial Banks are available from three sources: Form X return of SCBs (including RRBs) as at end-March, Statistical Tables Relating to Banks in India (STB) based on the audited accounts of the SCBs (excluding RRBs) as at end-March, and Section 42 returns (as on last reporting Friday/last Friday of March).

110. For the compilation of the FOF accounts of SCBs (including RRBs), the Form X returns are primarily used and supplemented with the STB and Section 42 returns. For the RRBs, the consolidated data provided by FIDD is used as supplements.

111. As sectoral details for most of the instruments are not available from these three sources, these details are estimated by making use of the results of different surveys on bank credit, deposits and investments (viz., Basic Statistical Returns (BSR) 1, 4 and 5). The procedure of compilation of the accounts for the sub-sectors is detailed below:

Liabilities

Currency and deposits

Deposits

112. Data on deposits with SCBs (including RRBs) are obtained from the Form X returns. The total deposits are disaggregated into current, saving and fixed deposits. Further, current and fixed deposits are disaggregated into deposits from Banks and 'Others‘. The sectoral ownership of these deposits is estimated on the basis of the respective shares in the 'Composition and Ownership of deposits with scheduled commercial banks (including RRBs) as on end-March‘; available through the BSR-4 Census10 and published in the STB.

Debt Securities

113. Banks are allowed to raise funds through subordinated debt. This data is obtained from the STB.

Loans

Borrowings and calls received in advance

114. The data relating to banks‘ borrowings furnished in the Form X are classified as borrowings from

a. Banks in India

  1. RBI

  2. SBI

  3. Associates of SBI

  4. Other Commercial Banks

  5. Co-operative Banks

b. From banks outside India

c. From financial institutions in India

  1. IDBI

  2. NABARD

  3. EXIM Bank

  4. Others

d. From financial institutions outside India

Equity and Investment Fund Shares

Paid-up capital

115. Data on the paid-up capital are obtained from Form X returns which are available for five groups of banks, viz., (a) SBI and its subsidiaries, (b) Public Sector banks, (c) Private Sector Banks, (d) RRBs, and (e) foreign banks. The paid-up capital of commercial banks is allocated among (a) the central government, (b) banks, (c) financial institutions, and (d) household sector.

116. The shareholding pattern of State Bank Group, Nationalised Banks, Old and New Private Sector Banks that is provided in the Statistical Tables relating to Banks in India is used for allocating the equity holding of these Banks. Paid–up capital of the RRBs is held by the central government, state governments and commercial banks, in the ratio 50:15:35 and allocated accordingly.

Other accounts payable

Bills payable

117. Details of this item are given under bills payable in India and bills payable outside India. The latter part is shown against the ROW sector.

Branch adjustments

118. Data under this head are available for branch adjustments (a) with offices in India, and (b) with offices outside India. While the second category represents transactions with the ROW sector, the first category represents intra-commercial bank transactions. An item 'branch adjustments among offices in India‘ also appears under 'assets‘ side. This item which appears under assets and liabilities sides represents the same type of transaction. Normally these should match with each other, as the liability of one branch on this account would be the corresponding asset of the other and vice versa. However, in practice these two do not match with each other. Therefore, the higher amount of 'branch adjustments among offices in India‘ either under assets or liabilities, is retained and the smaller amount is revised upwards by adjusting the 'other assets‘ or 'other liabilities‘, as the case may be.

Miscellaneous liabilities

119. Data on this item are available in the Form- X return. It comprises various items of financial and non-financial nature, such as unclaimed dividends, staff gratuity account, investment fluctuation reserves, provision for tax liability, reserve for bad and doubtful debts, special reserves, secret reserves and interest accrued on deposits outstanding. However, details on these components are not available for bifurcating them into financial and non-financial transactions. The item 'other intangible (though termed as tangible) assets‘ includes interest accrued on investments, advance tax paid less provision and tax deducted at source, sundries, like suspense, temporary advance, security deposits, clearing and other adjusting accounts. This item also includes both financial and non-financial items, for which no details are available. The item 'miscellaneous liabilities‘ net of other assets‘ (after adjusting for the reporting difference are presented as financial flow.

Reserve Fund, Other Reserves and Balance of Profit

120. Changes in these items represent net saving of the commercial banks during the fiscal year. The balance of amount against share premium account though received from the investors for which no liability arises for the banks is also shown along with savings. The amount of forfeited shares which also should be shown under this category is shown along with paid-up capital for want of details.

Financial Assets

Currency and deposits

Currency (Cash on hand)

121. Data on cash in hand are taken from the Form X return. This item is split into bank notes and government notes, as described in the accounts of the RBI. Bank notes are classified against the banking sector while the government notes are shown under the government sector.

Deposits

Inter-bank positions (Balances with RBI and banks, money at call and short notice)

122. Data on these items are obtained from the Form X return under the sub-heads (a) balances with the RBI, (b) balance with other banks - in current account, and (c) money at call and short notice. Balances with other banks in India (current account) include that with SBI and associates, other commercial banks and co-operative banks. The item ―Fixed deposits with Banks (including co-operative banks)‖ shown under Investments in the Form X returns is also included under this head.

123. Money at call and short notice represents the amount made available to others by way of loans or deposits repayable at call or short notice of a fortnight or less. Commercial banks and other financial institutions participate in the call money market. Based on the details available in Form X, the money at call is allocated between commercial banks and other financial intermediaries.

Debt securities and Equity

Investments

124. This item in Form X records banks‘ investments in (a) Treasury Bills, (b) Other Central Government Securities, (c) State Government Securities, (d) other approved securities (e) shares and debentures of companies and corporations not included in (d), (f) fixed deposits in banks (including co-operative banks) and (g) other investment in India. The Form X returns shows 'fixed deposits with banks‘ under 'investments‘. However, fixed deposits are not considered while working out the sectoral details of investments. The survey of investments of scheduled commercial banks as on 31st March (BSR-5) published by RBI (BSR, Table 8.1), classifies the total investments of banks into investments by offices in India and investment by foreign offices of Indian banks. The latter are not taken into account in working out the sectoral estimates. This survey is, however, available with a one year lag.

Loans

Loans and Advances

125. Total bank credit consists of 'loans, cash-credits and overdrafts‘, 'inland bills purchased and discounted‘ and 'foreign bills purchased and discounted‘. Loans, cashcredits and overdrafts represent all types of credit facilities (other than the bills) such as demand loans, term loans, cash credits, overdrafts and packing credits. Inland bills represent bills drawn and payable in India including demand drafts and cheques, purchased and discounted and foreign bills include all import and export bills including demand drafts drawn in foreign currencies and payable in India. Data on total bank credit, collected from the Form X return/ Statistical Tables, are available separately in respect of advances to banks and to others.

126. The information on bank credit according to organisation and occupation (industrial activity), as on 31st March every year, are available in the BSR-1 publication of RBI.11 The occupational groups classified for the purposes are: (i) agriculture, (ii) Industry, (iii) transport operations, (iv) professional and other services, (v) personal loans, (vi) Trade, (vii) finance and (viii) others. Under these occupational groups, the Household Sector covers pure households (individuals, HUF, etc) and organizations like proprietary concerns, partnership, joint families, joint liability groups (JLG), NGOs, Trusts etc. Besides, the amount shown under credit limits of ₹25,000 and less is also shown as loans to the household sector.

127. Advances to commercial banks and intra-commercial bank balances (including fixed deposits) appearing under assets have corresponding entries under liabilities as borrowings from commercial banks in India and intra-commercial bank deposits. Due to differences in reporting, figures appearing against these heads under assets and liabilities do not match with each other. Therefore, to minimise the discrepancy arising due to differences in reporting the higher amount is maintained either under assets or liabilities by revising upwards the lower amount by adjusting other assets or other liabilities as the case may be.

128. Details of branch adjustments among offices in India and branch adjustments with offices outside India are available in Form X. The former category is an intra-bank transaction while the latter is taken as a claim on the Rest of the World. As stated earlier, branch adjustments among offices in India appearing under assets should match with the similar item on liabilities side. As it does not match in practice, the higher figure is maintained under assets and liabilities by revising upwards the lower figure.

Other accounts receivable/payable

Other intangible assets

129. While describing the item 'other miscellaneous liabilities‘ mention is made about the type of financial transactions included under 'other intangible assets‘. The excess of other assets over miscellaneous liabilities, after excluding the amount transferred to inter-commercial bank transactions (viz., deposits, borrowings, branch adjustments), is shown as a financial transaction.

Non-Financial Assets

Premises, furniture, fixtures and other fixed assets

130. Data on this item are taken from the Form X return/STB. The item represents net physical assets of commercial banks and the variation over the two consecutive periods is the net capital formation of commercial banks.

Capitalised expenses and non-banking assets acquired in satisfaction of claims

131. This item, obtained from the Form X return, is classified as a non-financial transaction. Capitalised expenses include preliminary expenses, organisational expenses, share selling commission, brokerage, loss incurred and any other expenditure. This is, therefore, shown as other capital payment.

Excess of liabilities over assets

132. The Form X return presents the liabilities and assets of all commercial banks in respect of their Indian business only. Therefore, the totals of liabilities and assets given in the Form X return do not match with each other. The difference, i.e., the excess of liabilities over assets is shown as net foreign assets and considered as a claim on the rest of the world.

Co-operative Banks

133. This sub-sector includes the deposit-taking, State Co-operative Banks (StCBs) District Central Co-operative Banks (DCCBs), Primary/Urban Co-operative banks (PCBs/UCBs).

134. The data on assets and liabilities as at end-March for StCBs and DCCBs are available in Form IX returns from the FIDD, RBI. The compilation methodology is given in detail below. The 'Statistical Statements on the Co-operative Movement in India‘ published by the NABARD, which provides the sectoral details of various financial instruments, is available with a time lag. Therefore, the Statistical Statements is primarily used for obtaining the ownership pattern wherever they are not available from the data provided by FIDD in respect of StCBs and DCCBs and DCBR in respect of UCBs.

State and Central Co-operative Banks

Currency and deposits

Deposits

135. Deposit data by type of deposits, i.e. current, savings, fixed and other deposits (cash certificates, recurring deposits, etc) are available for StCBs and DCCBs. For each of these deposit types, ownership by (a) DCCB, (b) Non-financial corporations, (c) Other Societies, (d) Individuals, firms and companies, and (e) Reserve fund deposits maintained by societies are available separately. The ownership of other categories is obtained by applying ratios from the Statistical Statements of the latest available year.

Loans (Borrowings)

136. The break-up of borrowings into borrowings from (a) RBI (b) SBI (c) StCB, (d) DCCB, (e) NABARD and (f) Others is available. Borrowings from other sectors is obtained using shares in the Statistical Statements of the latest available year.

Equity and Investment Fund shares

Equity (Paid-up capital)

137. The Form IX returns of the FIDD provides the paid-up capital of StCB and DCCBs as held by (a) Individuals, (b) Cooperative Societies, (c) State Government and (d) Others. Ownership of other sectors is obtained by applying shares as per the latest available Statistical Statements. The Statistical Statements provide the details of paidup capital held by co-operative credit and non-credit societies.

Other accounts receivable/payable (Other Demand and Time liabilities)

138. The data on 'other demand‘ and 'other time liabilities‘ are shown under this head.

Reserve funds and other reserves

139. The Form IX returns of FIDD provide data on (a) Statutory reserves, (b) Agricultural Credit Stabilisation Fund, (c) Dividend equalization Fund, (d) Special Bad debt reserve, (e) Bad and doubtful debts reserves, (f) investment depreciation reserves and (f) Other Funds and Reserves. The change in these reserves is shown as the saving of the StCBs/DCCBs.

Financial Assets

Currency and deposits

Currency (Cash in hand)

140. Data on this item are readily available. This item is split into (a) RBI notes, and (b) one rupee notes and coins as per the usual procedure.12

Deposits

Balances with banks

141. This item includes total amount of balance with banks. The Form IX returns provides the segregation of these balances with (i) RBI, (ii) SBI and its subsidiaries (iii) banking companies, and (iv) Co-operative banks viz., State Co-operative bank of the State concerned and Central Co-operative bank of the District concerned and (v) other Co-operative banks. This item, which includes fixed deposits with commercial banks and co-operative banks is shown separately in FOF accounts.

Call deposits (Money at call & Short Notice)

142. Call deposits are the amounts kept with banking institutions as deposits which can be withdrawn at call or short-notice, irrespective of the period of notice. The Form IX returns give the details of the Money at call and short notice with (a) SBI and its associates, (b) Banking Companies, (c) StCB of the State concerned, (d) DCCB of the district concerned and (e) Other Co-operative Banks.

Debt securities

143. The Form IX return, under the head 'Investments‘ provides the details of investment in debt securities, namely (a) Central Government Securities (including treasury bills), (b) State Government securities, (c) Debenture of SCARDBs (earlier known as Land Mortgage Banks) and (d) other Trustee securities.

Loans

144. The Form IX provides data on loans and advances made for (a) agricultural operations (short term and medium term), (b) Marketing of Crops, (c) Weaver Societies, (d) Other industrial purposes (short term and medium term), and (e) Other purposes. The recipients of loans and advances are classified against (i) co-operative societies, (ii) individuals and others. Loans to the first category are split into (a) co-operative banks and credit societies, and (b) cooperative non-credit societies, on the basis of data obtained from the special returns. The second category of borrowers covers all individuals as well as other institutions. In the absence of further details, the category 'individuals and others‘ is classified as 'households‘.

Equity and investment fund shares

145. The Form IX return provides data on investments out of the Principal/Subsidiary Partnership Funds in the shares of (i) StCBs, (ii) DCCBs, and (ii) other co-operative societies.

Other accounts receivable / payable

146. This item includes 'bills purchased and discounted‘, 'interest receivable on loans and advances‘, interest overdues and 'other assets‘. "Overdue interest" relates to the amount of interest that was due on loans and advances during the year but not received and it does not relate to interest accrued on investments. The total amount of interest due is allocated to 'societies‘ and to 'households‘ in the proportion of loans outstanding against these two categories of borrowers.

Non-Financial Assets

Fixed assets

147. Fixed assets (shown as premises, furniture, fixtures and other fixed asstes) represent the capital stock as at the end of the year and the variation in it, is the net capital formation of these banks during the year. As such, variation in fixed assets is taken to represent net fixed assets formation.

Difference between assets and liabilities

148. The figure appears under liabilities or assets depending on whether the assets are more or less than liabilities. This item is clubbed together with reserve fund and other reserves/funds, as a non-financial transaction.

Urban Cooperative Banks (UCBs)

149. The data on assets and liabilities as at end-March for UCBs are collected from DCBR. The sectoral details, wherever not available, are worked out by applying shares as per the data for StCBs and DCCBs.

Deposit-Taking Non-Banking Financial Companies (NBFC-D)

150. This sub-sector includes all deposit taking NBFCs registered with the DNBR in the RBI. The consolidated data on assets and liabilities of these NBFCs are provided by the DNBR. The compilation of the sectoral FOF accounts are as follows:

Deposits

151. Currently, the entire deposits held by the NBFC-D are classified as held by households.

Debt securities

152. The debentures issued by the NBFC-D are segregated as subscribed by (a) Mutual Funds, (b) Banks, (c) NBFCs, and (d) Others.

Loans (Borrowings)

153. The data on borrowings by NBFC-D from (a) Banks, (b) Financial Institutions and (c) Government are available. Further, data on subordinated debt and 'other borrowings‘ are available, for which the sectoral details are not available.

Currency and deposits

154. The DNBR data provides cash on hand and deposits of the NBFC-D with banks.

Debt securities

155. The investments of NBFCs in (a) Government securities, (b) Commercial Paper and (c) Debenture and Bonds are available and included under this head.

Loans and advances

156. The data on loans and advances are available, though the sectoral details are not available.

Equity and Investment fund shares

157. Data on investment in (a) equity shares, (b) preference shares, and (c) units of mutual funds are available, though the sectoral details are not available. The item 'other investments‘, in the absence of details, are included under this head.

Other accounts receivable / payable

158. The items 'Other Current Assets‘ and 'Other Assets‘ are included under this head.

Deposit-Taking Housing Finance Companies (HFC-D)

159. This sub-sector includes all deposit taking HFCs registered with the National Housing Bank (NHB). The NHB provides the consolidated data on all financial assets and liabilities and the sectoral details to RBI, which are used for compilation of FoF accounts for their sector.

1.2.3 Mutual Funds

160. This sub-sector includes the Mutual Funds registered with the SEBI. The SEBI provides the consolidated data on all financial assets and liabilities and the sectoral details to RBI for the compilation of the FOF accounts for this sub-sector.

1.2.4 Other financial intermediaries, except insurance corporations and pension funds

Primary Credit Societies

161. 'Primary credit societies‘ comprise primary agricultural and non-agricultural credit societies. Certain primary non-agricultural credit societies, such as, urban banks, employees‘ credit societies, which satisfy certain provisions of the Banking Regulation Act, 1949 and Section 2(c) (iv) of the RBI Act, are classified as primary co-operative banks. The compilation of the FOF accounts of the PCBs/UCBs was explained above. Grain banks are also a type of primary agricultural credit societies.

162. The Statistical Statements on the Cooperative Movement in India is published with a lag. Therefore, the FOF accounts for these societies for the years under review are estimated by applying the same growth as observed under the relevant financial instruments of the DCCBs as per the Form IX return of the FIDD, RBI. These estimates undergo revision as and when the Statistical Statements of the particular year are released by NABARD.

State Cooperative Agriculture and Rural Development Banks (SCARDBs) and Primary Cooperative Agriculture and Rural Development Banks (PCARDBs)

163. For the FOF accounts of the SCARDBs and PCARDBs, the data provided by NABARD is used. For SCARDBs, the sectoral details are obtained by applying the shares as observed in the StCB data furnished by FIDD. For PCARDBs, the sectoral details are obtained by applying the shares as observed in the DCCB data furnished by FIDD.

Industrial Co-operative Banks (State/Central)

164. The Statistical Statements on the Cooperative Movement in India, which is the primary source for these cooperatives, is published with a lag13. Therefore, the FOF accounts for these societies for the years under review are estimated by applying the same growth as observed under the relevant financial instruments of the DCCBs as per the Form IX return of the FIDD, RBI. These estimates undergo revisions as and when the Statistical Statements of the particular year are released by NABARD.

Financial Corporation and Companies

165. The coverage of the sub-sector 'financial corporations and companies has improved over time on account of the availability of data. The sub-sector includes NABARD, EXIM Bank, SIDBI, REC, HUDCO, SFCs, and SIDCs. The Annual Report and Accounts of the corporations form the basic source in respect of most of the financial corporations. The data pertaining to NABARD, EXIM Bank, SIDBI, REC, and HUDCO are also obtained through special returns from these institutions. The consolidated data for the SFCs are provided by the SIDBI.

166. Earlier, the studies on finances of 'financial and investment companies‘ published in the RBI Bulletin formed the basic source for the financial companies. Since the studies present the data only for a sample of non-government financial companies, the global figures were estimated on the basis of the share of the paid-up capital covered in these studies. In the revised methodology, however, with the DNBR providing consolidated balance sheet information of both deposit-taking (NBFC-D) and non- deposit taking systemically important NBFCs (NBFC-NDSI) and the NHB providing consolidated balance sheet information of HFCs, the studies on finances of 'financial and investment companies‘ are used to estimate the financial flows in the rest of the sector excluding the NBFCs and HFCs.

1.2.6 Insurance corporations

167. This sub-sector includes all the insurance companies/corporations which are engaged in life insurance business, general insurance business, namely, capital redemption insurance, marine insurance, fire insurance and miscellaneous insurance, and deposit insurance business. The life insurance business carried out by post offices is, however, not included here but is covered in the central government‘s accounts.

168. The annual reports of the companies/ corporations form the basic source for the data on their assets and liabilities. The Insurance Regulatory and Development Authority of India (IRDAI) provides consolidated balance sheet data (along with sectoral details) pertaining to the life and non-life insurance companies which is used in the compilation of the FOF accounts of this sector.

1.2.7 Pension funds

Provident Funds

169. This sub-sector covers (i) the employees‘ provident fund of the non-government and certain semi-government organizations whose accounts do not get reflected in the budgets of central and state governments, (ii) the contributory pension fund, (iii) the National Pension System (NPS), and (iv) the deposit linked insurance fund maintained by certain trusts of provident funds.

170. The first category comprises the employees‘ provident fund of the RBI, commercial banks, coal mines, Assam tea plantations, seamen, industrial establishments covered under the Employees Provident Fund (EPF) Scheme, 1952,14 local authorities (including port trusts), IFCI, SFCs, LIC, the Air India, non-government educational institutions, labour boards and 'financial and investment‘ companies. The second category covers the contributory pension fund of the employees of coal mines, Assam tea plantations, and EPF scheme.

171. The third category, the NPS reflects the Government‘s effort to find sustainable solutions to the problem of providing adequate retirement income. As a first step towards instituting pensionary reforms, Government of India moved from a defined benefit pension to a defined contribution based pension system by making it mandatory for its new recruits (except armed forces) with effect from 1st January, 2004. Since April 1, 2008, the pension contributions of Central Government employees covered by the NPS are being invested by professional Pension Fund Managers in line with investment guidelines of Government applicable to non-Government Provident Funds.

172. As at end-January 2014, twenty eight (28) States/UT Governments have notified the NPS for their new employees. Of these, twenty four states have already signed agreements with the intermediaries of the NPS architecture appointed by PFRDA for carrying forward the implementation of the NPS. The other States are in the process of finalisation of documentation. Since May 1, 2009, the NPS has been made available to every citizen on a voluntary basis. The fourth category, the deposits linked insurance fund, is maintained by the EPF organization, coal mines and Assam tea plantations.

173. The provident funds of central and state governments‘ employees (known as State Provident Funds) and the Public Provident Fund (under small savings) are not included in this sector but covered under the accounts of the central and state governments.

174. The basic sources providing the data on provident fund/pension fund are the special returns from EPFO, coal mines and Assam tea plantations, seamen, BSR-5 in respect of commercial banks, data from the DGBA (RBI), dock labour boards, LIC and Air India.

175. The contribution of employees and the employers into the Provident Funds, the contributory pension fund, the National Pension System and the deposit-linked insurance fund are the sources of funds for this sub-sector. The household sector is treated as the claimant of these funds.

176. The special returns of the provident funds provide data on investments in (i) central government securities, (ii) state government securities, (iii) government guaranteed securities (e.g. bonds of financial corporations, port trusts, and SEBs), (iv) small savings (i.e. post office savings and time deposits and national savings certificates), (v) deposits with commercial banks, and (vi) special deposits with central government. In the case of the RBI, the employees‘ provident fund is maintained as deposits with the RBI. The investment pattern of provident funds of commercial banks is directly available in BSR-5.

2.3 General government

177. The Government Sector comprises (a) central government and autonomous bodies, (b) state governments and union territories, (c) local authorities (covering municipal corporations, municipalities and panchayats). Financial undertakings of the public sector are not included here as they are covered under banking and other financial institutions sectors. The post office savings banks are, however, included in the accounts of the central government as the liabilities of the post offices are borne by the central government. The procedure adopted for the compilation of the accounts of each sub-sector is described in the following paragraphs.

2.3.1 Central government including social security

178. The 'Economic and Functional Classification (EFC) of the Central Government Budget‘ published by the Department of Economic Affairs, Ministry of Finance, Government of India, forms the basic source of data to compile the accounts of this subsector. Unlike in the case of financial institutions and, for which the balance sheet data are available, the Economic Classification presents a set of six accounts reclassifying data given in the budget of the central government. Accounts 4 and 5 of the Economic Classification provide data on changes in financial liabilities and assets of the central government administration.

179. The EFC, however, does not present the sectoral break-down of market loans, treasury bills, small savings, other types of borrowings, as also the disbursement of funds through investments, and loans and advances. For arriving at the sectoral breakdown of some of the government‘s sources and uses of funds, the information from various other sources is used. The sectoral particulars of these various items are given below.

Liabilities

Currency and deposits

Deposits (Small savings)

180. Small savings comprise savings deposits with post offices and savings certificates. These include post office savings bank deposits, time deposits (1, 2, 3 and 5 years), recurring deposits, monthly income scheme deposits and senior citizen saving scheme, National Savings Certificates (NSC) and Public Provident Fund (PPF). The money raised through the PPF is shown separately under State Provident Fund. Regarding the ownership details, these are derived on the basis of investing sector‘ accounts. Thus, the residual after deducting the above sectors‘ investment in small savings from the total small savings, is assumed to have been invested by the Household Sector.

Debt securities

Treasury Bills

181. Data on treasury bills (14-day to 364-day) (net) are available in the EFC. Ownership particulars of these bills are available in the return received from the Department of Government and Bank Accounts (DGBA) of the Bank.

Market Loans

182. The gross receipts of market loans and their repayments are given in the Economic Classification. The sectoral break-up of the market loans, according to various sectors is worked out on the basis of data available on "Ownership of Central Government securities‘. The categories of ownership given therein are (i) the Reserve Bank of India (own account), (ii) Scheduled Commercial Banks, (iii) Primary Dealers, (iv) Insurance Companies, (v) Financial Institutions, (vi) Mutual Funds, (vii) Provident Funds, and (viii) Others.

External debt

183. The Economic Classification presents gross borrowings of the central government from the rest of the world and their repayments. It includes the government‘s borrowings from various international financial organisations/agencies, foreign governments as also the special credits from oil exporting countries. Gross borrowings minus repayments are shown as the net borrowings of the central government from the Rest of World Sector.

State Provident fund

184. The provident fund of the employees of the central government, and the amounts collected by the government from public under the Public Provident Fund (PPF) Scheme, 1968, are covered under this head. This is treated as a claim of the Household Sector.

Other debt

185. Miscellaneous capital (debt) receipts presented in the Economic Classification along with one rupee notes and coins are covered under this head. One rupee notes and coins represent the liability of the central government in the form of currency consisting of (i) one rupee notes / coins in circulation, (ii) small coins in circulation, and (iii) commemorative coins issued by the government in higher denominations. Data on one-rupee notes and coins are collected from tables relating to Money Stock Measures published in the RBI Bulletin. The holding of one-rupee notes and coins by the RBI has been added to this amount for deriving the total liability of the central government under this head. The one-rupee notes and coins are split-up into claims of various sectors using the estimated holdings of rupee coin and small coins as presented in each sector‘s accounts.

Financial Assets

Currency and deposits

Currency (Cash balances)

186. The Economic Classification presents the total cash balances of the central government in its Account No. 6 as increase/ decrease in cash balances. This head includes the cash in treasuries and deposits with the RBI. Further, the total cash in treasuries is split-up to show bank notes and government notes and coins.

Loans & Advances

187. Particulars of disbursement of loans and advances are given against (i) loans for capital formation, and (ii) other loans. Institutional details of disbursement of loans for capital formation and other loans are available against (i) states and union territories, (ii) local authorities, (iii) non-departmental commercial undertakings – financial concerns and others, and (iv) others. The break-up of 'financial concerns and others‘ under loans for capital formation, however, are not available. The category 'others‘ includes co– operative societies, private sector companies, and households. As the details on repayments are available only against 'states and union territories‘ and 'others‘, the sectoral particulars of loans and advances net of repayments are derived for sectors other than state governments wherever available, on the basis of (i) accounts of financial corporations, government and non-government companies, (ii) the budget of the central government in the case of repayments by foreign governments, (iii) Finance Accounts of the Union Government, and (iv) the Explanatory Memorandum to the Budget. Financial corporations and government non-financial undertakings show, in their accounts, their borrowings from the central government which are taken as its loans to them. Data on loans to households comprising loans to government servants are compiled on the basis of the details given in the Receipts Budgets of Central Government Budget. Later, these data are revised in the light of the Finance Accounts or the Combined Finance and Revenue Accounts which are available subsequently.

Equity and Investment Fund shares (Investments)

188. Account No. 4 of the Economic Classification presents the data on changes in financial assets of the central government. This account provides the details of investments into shares of government and other concerns. The government concerns are further sub- divided into financial concerns and other concerns. Financial concerns consist of banking institutions, financial corporations and insurance corporations in the public sector, while others relate to the non-financial non-departmental undertakings.

Other accounts receivable

189. Data on subscriptions to international financial organizations are given in the Economic and Functional Classification. This item is shown as a claim on the 'Rest of the World‘ Sector.

2.3.2 State government and Union Territories including social security

190. The Combined Finance and Revenue Accounts (CFRA) of union and state governments in India published by the Comptroller and Auditor General of India, Government of India, gives data in respect of all state governments but this publication is available after a lag. The primary source to prepare the CFRA is Finance Accounts of state governments published by the Auditor General of each state which is also available with a lag of about two years. The studies on 'Finances of State Governments‘ prepared by the Department of Economic and Policy Research (Fiscal Analysis Division) provide the data relating to state governments and two union territories with legislature.

191. Data presented in the RBI Studies, however, do not provide all the necessary details for compiling the FoF accounts of this sub-sector. These are supplemented with the data from budget documents and other sources, like information on 'Ownership of State Government Securities‘, 'Finance accounts of State Governments‘ and the accounts of other sectors.

192. Budgets of the state governments are used to obtain the particulars of their debt under the following heads: (i) internal debt comprising market loans, power bonds, special securities issued to NSSF, loans from banks/financial institutions, ways and means advances from RBI and others including land compensation and other bonds; (ii) loans from the Centre and (iii) public account including State PF, reserve fund and deposits and advances; and (iv) Contingency Fund.

Liabilities

Debt Securities (Market Loans)

193. Market loans include state development bonds floated in the market by the state governments. The Budget documents or the 'Finance Accounts‘ of each state government present only gross receipts and repayments of market loans. The particulars of ownership are derived from the information on 'Ownership of State Government Securities‘ published in the 'Handbook of Statistics of the Indian Economy.

Loans (Borrowings)

194. Borrowing by way of (i) ways and means advances from the RBI, and (ii) special securities issued to NSSF (iii) loans and advances from banks/ financial institutions and (iv) loans and advances from the central government are covered under this sub-head

Provident funds

195. This instrument known as unfunded debt, includes provident funds of state government employees (titled as State Provident Funds), State insurance and pension funds and others. State provident funds are treated as claims of the Household sector and shown separately.

Financial Assets

Currency and deposits

Currency (Cash Balances)

196. Data on cash at treasuries, local remittances, balances at the RBI and other commercial banks are obtained from either the Combined Finance and Revenue Accounts or the Finance Accounts of each state government. Amounts shown against (i) cash with departmental offices, and (ii) permanent cash imprest are also included under cash balances forming currency held by the state governments‘ administration.

Loans and Advances

197. The study on 'Finances of State Governments‘ provides the details on total loans and advances of all the state governments but these are not sufficient for the purpose of FOF accounts. Therefore, complete details of disbursements and receipts of loans and advances are culled out from the Finance Accounts of each state government. The particulars of loans are reclassified according to the following sub-sectors: (i) Cooperative Banks/Societies, (ii) Local Authorities, viz., Panchayat Raj Institutions, Municipal Councils/Corporations, (iii) Statutory Corporations, (iv) Government Companies/Corporations including State Power Utilities, (v) Urban Development authorities, (vi) Housing Boards, (vii) Households, and (viii) others.

Investments (Debt and Equity)

198. The investments of state governments in share capital and debentures and securities of central government are covered under this head. However, investments from (i) cash balance investment account, (ii) sinking fund investment account, (iii) other accounts, of the state governments in central government securities/T bills would form intra-government investments. As in the case of loans and advances, the sectoral particulars are culled out from the Finance Accounts of each state government against the following sub-sectors; (i) Statutory Corporations, (ii) Rural Banks, (iii) Government Companies, (iv) Joint Stock Companies and partnership firms, (v) Cooperative Banks and Societies, and (vi) Industrial Financial Institutions. Besides these, data relating to investment in securities out of earmarked funds and investment in treasury bills are obtained from the Finance Accounts of the State Governments.

2.3.3 Local government including social security

199. This sub-sector includes municipal corporations, municipalities, and panchayats. The FOF accounts of this sector would be derived, to the extent possible, from those of the four domestic sectors described earlier. The FOF accounts would be extended as and when new data become available.

2.4 Rest of the World

200. The domestic sectors, viz., non-financial corporations, financial corporations, general government, and households have transactions with foreign governments, foreign central banks, major foreign commercial banks, various international agencies and institutions like IMF, IBRD, IDA, ADB, IFC, and non-resident individuals. All transactions of the domestic sectors with foreign units that are effected through the medium of money and credit are recorded in the accounts of the Rest of the World (ROW). The FOF accounts of the ROW are derived from the Balance of Payments (BOP) statistics.

201. The RBI publishes India‘s BOP statistics which is a statistical statement that comprises transactions between residents and non-residents during a period. It consists of the 'goods and services‘ accounts, the primary income account, the secondary income account, the capital account and the financial account. The financial account of the BoP records the transactions of the domestic sectors with foreign entities, leading to changes in the country‘s foreign assets and liabilities. In other words, the sum total of net transactions under the current and capital account represents net lending (surplus) or net borrowing (deficit) by the economy from the Rest of the World, which is reflected in the financial account as net outflows or inflows of capital.

202. The BoP account is presented from India‘s point of view and the transactions are recorded as credits or debits, the former constituting increase in liabilities/decrease in assets and the latter covering decrease in liabilities/increase in assets. As per the BPM6, capital account transactions are to be recorded on a gross basis, while financial account transactions (which also include reserve accounts) are to be recorded on a net basis. The FOF accounts, however, are constructed from the stand point of the ROW Sector. As such, the credits and debits recorded in the balance of payments statistics become debits and credits, respectively, for the ROW. The major components of financial accounts include direct investment, portfolio investment, financial derivatives (other than reserves), and employee stock options (ESOPs), other investments, reserve assets (monetary gold), equity and investment fund shares, debt instrument and other financial assets and liabilities. The details of items appearing in the financial account are given in the BoP Manual for India, 2010 (/en/web/rbi/-/publications/balance-of-payments-manual-for-india-13013).

2.5 Household and Non-profit Institutions serving Household Sectors

203. The Household Sector is the residual sector which comprises all individuals, non-government non-corporate enterprises of farm business and non-farm business, like, sole proprietorships and partnerships, and non-profit institutions. Thus, it includes all the enterprises/economic units which are not covered in the other four domestic sectors of the economy. This sector does not have any single source of data on their assets and liabilities as on any particular date. The FOF accounts of this sector are, therefore, derived from those of the four domestic sectors described earlier.

204. The Household sector‘s share in a particular instrument is estimated against each of the instruments issued or held by each of the sub-sectors. For example, households' deposits with commercial banks are estimated on the basis of the survey of 'ownership of deposits with commercial banks‘; likewise, households‘ borrowing from commercial banks is estimated by utilising the data collected through BSR -1 return. For certain instruments, households‘ share is derived as a residual, viz., investments of other institutions/sectors in shares are deducted from the total amount issued to estimate the household sector‘s investment in shares during a year. The procedure of estimation of the household Sector‘s share in each of the instruments is explained in the methodology of the respective sub-sectors described earlier, e.g. estimation of households‘ contributions towards (a) deposits with non-banking financial companies is given under the sub-sector 'financial corporations‘, (b) Mutual fund units is presented in the Mutual Funds sub-sector which is compiled using SEBI data.

References

Commission of the European Communities, International Monetary Fund, Organization for Economic Cooperation and Development, United Nations, and World Bank, 1993, System of National Accounts 1993 (Brussels/Luxembourg, New York, Paris, and Washington). Available via the Internet: http://unstats.un.org/unsd/sna1993/toctop.asp.

———, 2004, Updates and Amendments to the System of National Accounts 1993 (Washington).

———, 2008, System of National Accounts 2008

International Monetary Fund (2000), Monetary and Financial Statistics Manual, October.

--- (2008) Monetary and Financial Statistics Compilation Guide, April

Reserve Bank of India, Annual Report, various issues.

---, Basic Statistical Returns of Scheduled Commercial Banks in India, various issues.

---, Database on Indian Economy, real time Handbook of Statistics on Indian Economy.

---, (1967), Financial Flows in the Indian Economy - 1951-52 to 1962-63, RBI Bulletin, March.

---, Report on Trend and Progress of Banking in India, various issues.

---, State Finances: A Study of Budgets, various issues

---, Statistical Tables relating to Banks in India


List of Formats

Format 1: List of Financial Instruments adopted in the Flow of Funds Accounts
(Rs Crore)
Code Financial Asset Sources/Uses of Funds
    Amount outstanding end-March
    2011 2012
F Total    
F1 Monetary gold and SDRs    
F11 Monetary gold    
F12 SDRs    
F2 Currency and deposits    
F21 Currency    
F22 Transferable deposits with    
  Commercial Banks    
  Cooperative Banks    
F29 Other deposits with    
  Commercial Banks    
  Cooperative Banks    
  NBFCs-D    
  HFCs-D    
F3 Debt securities    
  Short-term securities of    
  Non-Financial Corporations    
  Financial Corporations    
  Deposit-taking corporations    
  Insurance corporations    
  Pension funds    
  General Government    
  Long-term securities of    
  Non-Financial Corporations    
  Financial Corporations    
  General Government    
  Central Government    
  State Governments Local authorities    
F4 Loans    
  Short-term    
  Non-Financial Corporations    
  Financial Corporations    
  General Government    
  Long-term    
  Non-Financial Corporations    
  Financial Corporations    
  General Government    
F5  
F51 Equity    
  Listed Shares    
  Non-Financial Corporations    
  Financial Corporations    
  Unlisted Shares    
  Non-Financial Corporations    
  Financial Corporations    
  Other Equity    
F52 Investment fund shares/units    
  Money market fund shares/units    
  Other investment fund shares/units    
F6  
  Non-Life insurance technical reserves    
  Life insurance and annuity entitlements    
  Pension entitlements/Provident Funds    
F8 Other accounts receivable/payable    
F81 Trade credit and advances    
  Non-Financial Corporations    
  Financial Corporations    
  General Government    
  Households and NPISHs    

Abbreviations

ACU Asian Clearing Union
ADB Asian Development Bank
ADR American Depository Reserve / Asset Development Reserve
BIS Bank for International Settlements
BRBNMPL Bharatiya Reserve Bank Note Mudran Private Limited
BSR Basic Statistical Returns
CAG Comptroller and Auditor General of India
CGA Controller General of Accounts
CICB Central Industrial Cooperative Bank
CR Contingency Reserve
CSO Central Statistics Office
CFRA Combined Finance and Revenue Accounts, CAG
CGRA Currency and Gold Revaluation Account
CPSE Central Public Sector Enterprises
DBR Department of Banking Regulation, RBI
DCCB District Central Cooperative Bank
DCBR Department of Cooperative Banking Regulation, RBI
DEPR Department of Economic and Policy Research, RBI
DFM Division of Financial Markets, DEPR, RBI
DGBA Department of Government and Bank Accounts, RBI
DICGC Deposit Insurance and Credit Guarantee Corporation
DITF Division of International Trade and Finance, DEPR, RBI
DMC Division of Money and Credit, DEPR, RBI
DNBR Department of Non-Banking Regulation, RBI
DPE Department of Public Enterprises, MHIPE, GoI
DSIM Department of Statistics and Information Management
EEA Exchange Equalisation Account
EFC Economic and Functional Classification of the Union Budget
EPFO Employees Provident Fund Organisation
ESOP Employee Stock Options
EXIM Bank Export Import Bank of India
FAD Fiscal Analysis Division, DEPR, RBI
FCA Foreign Currency Assets
FIDD Financial Inclusion and Development Department, RBI
FSB Financial Stability Board
FSS Farmers‘ Service Society
GoI Government of India
ICB Industrial Cooperative Bank
IDBI Industrial Development Bank of India
IFCI Industrial Finance Corporation of India
IIFI Industrial Investment Bank of India
IMF International Monetary Fund
IRA Investment Revaluation Account
IRDAI Insurance Regulatory and Development Authority of India
JLG Joint Liability Groups
LAB Local Area Bank
LAMPS Large Scale Adivasi Multi-purpose Societies
LIC Life Insurance Corporation
MCA Ministry of Corporate Affairs, GoI
MHIPE Ministry of Heavy Industries and Public Enterprises, GoI
MFSM Monetary and Financial Statistics Manual
MFSCG Monetary and Financial Statistics Compilation Guide
MOSPI Ministry of Statistics and Programme Implementation, GoI
MPD Monetary Policy Department
NABARD National Bank for Agriculture and Rural Development
NAS National Accounts Statistics
NBFC-D Non-Banking Finance Companies – Deposit taking
NBFC-NDSI Non-Banking Finance Companies – Non-Deposit taking Systemically Important
NGO Non-Government Organisation
NHB National Housing Bank
NHC (LTO) National Housing Credit (Long Term operations) Fund
NIC (LTO) National Industrial Credit (Long Term Operations) Fund
NPS National Pension System
NSC National Savings Certificate
OCVA Other Changes in Value Account
OFI Other Financial Institutions
PAC Primary Agricultural Cooperative Credit Society
PCARDB (PLDB) Primary Cooperative Agriculture and Rural Development Bank (earlier known as Primary Land Development Bank)
PCB Private Corporate Business sector
PFC Power Finance Corporation
PFM Pension Fund Manager
PFRDA Pension Fund Regulatory and Development Authority
PPF Public Provident Fund
PSE Public Sector Enterprises
RBI Reserve Bank of India
RCF Report on Currency and Finance, RBI
REC Rural Electrification Corporation
ROW Rest of the World
RRB Regional Rural Bank
RTP Report on Trend and Progress of Banking in India, RBI
SBI State Bank of India
SCARDB (SLDB) State Cooperative Agriculture and Rural Development Bank (earlier known as State Land Development Bank)
SCBs Scheduled Commercial Bank
SEB State Electricity Board
SEBI Securities and Exchange Board of India
SFC State Finance Corporations
SICB State Industrial Cooperative Bank
SIDBI Small Industries Development Bank of India
SIDC State Industrial Development Corporation
SIIDC State Industrial Infrastructure Development and Investment Corporation
SNA System of National Accounts
SPV Special Purpose Vehicles
SPSE State Public Sector Enterprises
StCB State Cooperative Bank
STB Statistical Tables Relating to Banks in India, RBI
SWIFT Society for Worldwide Interbank Financial Telecommunication
UCB Urban Cooperative Bank
UFA Union Finance Accounts, CGA
UTI Unit Trust of India

1 The Manual would be updated on a periodic basis taking into account evolving changes in the national accounting framework and new data/data sources in line with the remaining recommendations of the Working Group.

2 Reserve Bank of India (1967), ‘Financial Flows in the Indian Economy - 1951-52 to 1962-63’, RBI Bulletin, March.

3 Published jointly by: Commission of the European Communities—Eurostat, IMF, Organization for Economic Cooperation and Development (OECD), United Nations (UN), and World Bank (1993).

4 The SNA, 2008 can be accessed at the link http://unstats.un.org/unsd/nationalaccount/sna2008.asp.

5 In cases where private databases are used, they would be indicated in the FOF accounts.

6 The shift to MCA21 database of the Ministry of Corporate Affairs would be explored.

7 For details on operating companies, please see the articles on “Finances of Non-Government Non-Financial Public and Private Limited Companies” published in the RBI Bulletin periodically.

8 Data from private databases are also used.

9 This is included under 'Gold coin and bullion‘.

10 The BSR-4, which was survey-based earlier, is a Census since 2012.

11 BSR-1 relates to gross bank credit and comprises term loans, cash credit, overdrafts, bills purchased and discounted, bills rediscounted under the Bill Market Scheme and also dues from banks, whereas, the bank credit data, based on returns under Section 42(2) of the RBI Act, 1934, is exclusive of dues from banks and bills rediscounted. The BSR-1 return is divided into two parts - Part A and Part B (termed as BSR-1A and BSR- 1B). Till 1998, the BSR-1A return covered accounts with individual credit limit of over ₹ 25,000. Consequent upon the revision in the cut-off credit limit from March 1999 survey, BSR-1A return for scheduled commercial banks other than Regional Rural Banks, covers accounts with individual credit limit of over ₹ 0.2 million. In the case of Regional Rural Banks, the cut off limit then was ₹ 25,000. The revision of cut off limit for classifying accounts in BSR-1A has been made as ₹ 0.2 million for Regional Rural Banks also from March 2002 onwards. In BSR-1A, information in respect of each of the borrowal accounts is collected on various characteristics, such as place (district and population group) of utilisation of credit, type of account, type of organisation, occupational category, category of borrower code, secured/unsecured loan code, fixed / floating rate of interest flag, rate of interest, credit limit and amount outstanding. In BSR-1B, information in respect of small borrowal accounts with individual credit limit up to ₹ 0.2 million is obtained from all scheduled commercial banks in consolidated form for broad occupational categories for two separate credit limit groups, i.e., ‘up to ₹ 25,000’ and ‘over ₹ 25,000 and up to ₹ 0.2 million’.

12 For details please see RBI Sub-sector

13 The latest available is for the year _________.

14 Came into force with effect from 1-11-1952 under the Employees’ Provident Funds and Miscellaneous Act, 1952.

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Economic Capital Framework of the Reserve Bank of India – Internal Review of the Framework

Contents
Select Abbreviations and Definitions
Executive Summary
1. Extant Economic Capital Framework
2. Global macroeconomic environment and its impact on central bank balance sheets
I. Global macroeconomic environment and its impact on central bank balance sheets
II. Overview of recent literature on central banks’ capital adequacy
III. Scale of balance sheet challenges faced by RBI
3. Review of the Extant Economic Capital Framework
4. Review of Economic Capital Framework – Recommendations
I. Risk parameterisation and Provisioning for market risk
II. Provisioning for credit risk and operational risk
III. Provisioning for monetary and financial stability risks
IV. Surplus Distribution Policy (SDP)
V. Impact of Recommendations
5. Summary of Recommendations
References
Box 1.1: The ECF of RBI – Salient Aspects
Annex I: Surplus Distribution Policy, Extant Economic Capital Frameworks and Accounting Standards of select Central Banks
Annex II: Profitability and equity of central banks – Impact of macroeconomic environment
Annex III: Rationale for computing variance covariance matrix from price returns
Annex IV: Comparison of risk buffers for market risk under proposed and extant ECF
Annex V: Impact of proposed recommendations on risk provisioning and surplus transferable over last five years

Select Abbreviations and Definitions

ADF Asset Development Fund
AE Advanced Economies
ARE Available Realized Equity
BCBS Basel Committee on Banking Supervision
BIS Bank for International Settlements
BoP Balance of Payments
bps Basis Points
CAGR Compound Annual Growth Rate
CB Central Bank
CF Contingency Fund
CGRA Currency and Gold Revaluation Account
CL Confidence Level
CRB Contingent Risk Buffer
EC Economic Capital
ECB European Central Bank
ECF Economic Capital Framework
ELA Emergency Liquidity Assistance
EMDE Emerging Market and Developing Economy
EWMA Exponentially Weighted Moving Average
ERM Enterprise-wide Risk Management
ES Expected Shortfall
FCA Foreign Currency Assets
FCVA Foreign Exchange Forward Contracts Valuation Account
FER Foreign Exchange Reserves
GFC Global Financial Crisis
GoI Government of India
G-sec Government of India securities
HQLA High Quality Liquid Assets
IFRS International Financial Reporting Standards
IMF International Monetary Fund
IRA-RS Investment Revaluation Account-Rupee Securities
IRA-FS Investment Revaluation Account-Foreign Securities
LCR Liquidity Coverage Ratio
LOLR Lender of Last Resort
MMLR Market Maker of Last Resort
MTM Marked to Market
NBFC Non-Banking Financial Company
P&L Profit and Loss
QE Quantitative Easing
RBI Reserve Bank of India
RTL Risk Tolerance Limit
VaR Value at Risk

Select Definitions in the context of RBI’s ECF:

Economic capital / Risk buffers The RBI’s risk equity comprising its Capital, Reserve Fund, risk provisions (CF and ADF), and revaluation balances (CGRA, IRA-RS, IRA-FS and FCVA).
Risk provisions/ Realized risk provisions Provisions made towards CF and ADF under Section 47 of the RBI Act.
Realized Equity/ Available Realized Equity (ARE) The components of RBI’s economic capital comprising its Capital, Reserve Fund, and risk provisions (CF and ADF).
Requirement for Realized Equity (RRE) The size of Realized Equity to meet the requirement for Contingent Risk Buffer (CRB) and shortfall in market risk buffers, if any.
Contingent Risk Buffer (CRB) Component of RBI’s realized equity to provide for monetary and financial stability, credit, and operational risks.
Revaluation balances The unrealized gains, net of losses, resulting from exchange rate, gold price and interest rate movements. These are represented as Revaluation Accounts on the balance sheet of RBI.
Capital Paid-up capital in accordance with section 4 of the RBI Act, 1934 (Notes to Accounts [XII.6.1] in RBI’s Annual Report 2023-24)
Reserve Fund Reserve Fund of ₹5 crore provided for in terms of Section 46 of the RBI Act which was supplemented with the valuation gains which accrued on account of an amendment to Section 33 (4) of the RBI Act in 1990-91 (Notes to Accounts [XII.6.2] in RBI’s Annual Report 2023-24)
Contingency Fund Provisions for meeting unexpected and unforeseen contingencies, including depreciation in the value of securities, risks arising out of monetary/ exchange rate policy operations, systemic risks and any risk arising on account of the special responsibilities enjoined upon the RBI (Notes to Accounts [XII.6.5a] in RBI’s Annual Report 2023-24)
Asset Development Fund Provisions for investments in subsidiaries and associated institutions and to meet internal capital expenditure (Notes to Accounts [XII.6.5b] in RBI’s Annual Report 2023-24)
CGRA Unrealized gains/losses on Foreign Currency Assets and gold due to movement in exchange rate and prices of gold (Notes to Accounts [XII.6.6a] in RBI’s Annual Report 2023-24)
IRA- Foreign Securities Unrealized gains/losses on foreign dated securities on daily revaluation (Notes to Accounts [XII.6.6b] in RBI’s Annual Report 2023-24)
IRA- Rupee Securities Unrealized gains/ losses on rupee securities on periodic revaluation (Notes to Accounts [XII.6.6c] in RBI’s Annual Report 2023-24)
FCVA Unrealized gains/ losses on outstanding forward contracts
Net income Gross income, net of expenditure, prior to risk provisioning.

A Executive Summary

1. The extant ECF (adopted in 2019 based on recommendations of the Expert Committee to Review the Extant ECF of RBI) has been reviewed in view of the Committee’s recommendation that the ECF may be reviewed every five years.

Overview of macroeconomic environment during last 5 years

2. The macroeconomic environment has been challenging owing to the pandemic, elevated global public debt, persistent inflation, rapid monetary tightening by central banks, volatility in financial markets, prolonged geopolitical tensions and geo-economic fragmentation.

Impact of central bank policy actions on their balance sheets (B/S)

3. Central banks adopted accommodative monetary policy in response to the pandemic, leading to expansion in their B/S size and concomitant B/S risks, followed by aggressive and rapid tightening, owing to persistent inflation. This resulted in many central banks reporting negative net interest income due to materialisation of repricing risk on account of asset liability maturity mismatch, besides suffering valuation losses on their securities’ portfolio, underscoring the need to maintain a robust capital position.

Review of the extant ECF

4. Despite the adverse macroeconomic developments and other challenges mentioned above, the Bank’s prudent Accounting Policies1 and the ECF have enabled RBI to augment its financial resilience, while also ensuring healthy transfer of surplus to the Government, at a time when many central banks have reported net losses, depleted their equity and suspended surplus transfers. Besides, consistent implementation of a rule-based, publicly disclosed ECF has helped build stakeholder confidence and trust in commitment towards maintaining Bank’s financial resilience.

Summary of proposed recommendations

5. As the ECF has broadly met its objectives, despite adverse external developments, the review proposes continuation of the broad principles underlying the extant ECF, and no major changes in risk assessment methodologies. However, the review highlighted that the transfer of surplus to the Government has not been as stable as was desirable. Besides, certain risk sources that were not included in the current framework as they were not significant, have now gained in importance and merit inclusion.

Accordingly, the major recommendations of the review are indicated below.

Market risk

6. Major recommendations on the assessment of capital requirement for market risk are listed below:

(i) While the requirement of economic capital for market risk may continue to be assessed using Expected Shortfall (ES) under stressed conditions, it is proposed to provide flexibility2 to the Central Board to maintain market risk buffers at any desired resilience level within the range of ES at 99.5% Confidence Level (CL) and ES at 97.5% CL.

(ii) An integrated approach may be adopted, wherein the off-balance sheet portfolio is also considered, together with the on-B/S portfolio, while computing market risk buffer requirement.

(iii) The requirement for market risk buffers may include Foreign Currency Assets (FCA) exposure in minor currencies.

(iv) While computing market risk buffer requirement using Expected Shortfall, the variance-covariance (VC) matrix of price returns may be computed directly, rather than indirectly via transformation of VC matrix of yield returns.

Credit risk and operational risk

7. Economic capital for credit risk (including on account of OFBS exposures) and operational risk may continue to be maintained as hitherto.

Monetary and financial stability risk

8. Currently, the buffers are maintained at the resilience level decided by the Central Board, subject to a range of 4.5% - 5.5% of B/S size.

9. In this regard, while the challenges from the global macroeconomic environment and geopolitical developments amplify the need for maintaining an optimal level of realized equity to credibly discharge the Bank’s mandate, the resilience demonstrated by the Bank in recent years despite the pandemic and its aftermath, reinforces the Bank’s ability to manage monetary and financial stability risks effectively.

10. Further, the implementation period of the extant ECF has seen considerable volatility in the transfer of surplus to the Government, as indicated by a Coefficient of Variation (CV) of 63.30 per cent3. It is observed that the existing range of 1.0 per cent provides very limited flexibility to the Central Board to smoothen the transfer of surplus to the Government.

11. In view of the above, it is proposed to widen the applicable range of buffer requirement for monetary and financial stability risks to 5.0 ± 1.5 per cent, with the objective of providing adequate flexibility to the Central Board in determining the buffers, taking into account the prevailing macroeconomic and other factors, while also smoothening the transfer of surplus to the Government.

Requirement of Realized Equity

12. The Contingent Risk Buffer (CRB), which provides for monetary and financial stability risks, credit risk, and operational risk, would, thus, be maintained within a range of 6.0 ± 1.5 per cent of the B/S size (as against the level of 6.5 per cent, with lower bound of 5.5 per cent under extant ECF). The Requirement of Realized Equity (RRE), would include the CRB and shortfall, if any, in revaluation balances, vis-à-vis the requirement for market risk buffers at the resilience level determined by the Central Board.

Surplus Distribution Policy (SDP)

13. The Surplus Distribution Policy (SDP) may continue to treat revaluation balances as non-distributable, while imparting primacy to bolstering RBI’s financial resilience to the desired level, with the residual net income being available for transfer to the Government. Further, the clause applicable in case of Available Realized Equity (ARE) being short of the lower bound of RRE has been made comprehensive by requiring that appropriate risk provisioning may be made by RBI to augment ARE to ‘at least’ its lower bound. The clause has been expanded to state that in case net income is inadequate to augment ARE to its lower bound, no surplus will be transferred (including in subsequent years) till at least the lower bound is achieved. The excess realized equity, i.e., ARE in excess of RRE, shall be written back from Contingency Fund (CF) to income at the time of finalization of Annual Accounts.

Impact of Recommendations

14. An analysis of risk provisioning as on March 31, 2025, under the extant and revised ECF, considering buffers for monetary and financial stability risks within the proposed range of 5.0 ± 1.5 per cent of the B/S size, is given in Table A below:

Table A: EC Requirement under extant and revised ECF – March 31, 2025 - % of B/S (₹ cr)
S.No. Risk type Parameter Requirement of EC Available EC Risk provisioning
Extant Proposed Extant Proposed
Ia. Market risk (On BS items) 99.5% CL 17.86% 18.91% 17.40% (RB)    
97.5% CL 14.44% 15.29%
Ib. Market risk (OFBS) 99.5% CL NA -2.19%
97.5% CL NA -1.77%    
I.
(Ia+Ib)
Market risk (total) 99.5% CL 17.86% 16.72% NA 0%
97.5% CL 14.44% 13.51% 0% 0%
II. Credit & op risk Extant 1% 1% 6.91% (RE)    
III. Monetary and financial stability risk Upper Bound 5.5% 6.5%
Lower Bound 4.5% 3.5%
IV.
(II+III)
Total non-valuation risks Upper Bound 6.5% 7.5% (-) 0.41%
₹ (-) 31,393
0.59%
₹ 44,862
Lower Bound 5.5% 4.5% (-) 1.41%
₹ (-) 1,07,647
(-) 2.41%
₹ (-) 1,83,901

15. The impact of the proposed recommendations on risk provisioning and surplus transferable over the previous years, considering the buffers for monetary and financial stability risks being maintained within the proposed range of 5.0 ± 1.5 per cent of the B/S size, is placed in Table B below.

Table B: Impact of proposed recommendations on risk provisioning (₹ crore)
  Jun 2020 Mar 2021 Mar 2022 Mar 2023 Mar 2024 Mar 20254
B/S size 53,34,793 57,07,669 61,90,302 63,44,756 70,47,703 76,25,422
Level at which Realized Equity maintained 5.50% 5.50% 5.50% 6.00% 6.50% -
Risk provisioning 73,615 20,710 1,14,667 1,30,876 42,820 -
Surplus transferred 57,128 99,122 30,307 87,416 2,10,874 -
Component-wise additional risk provisioning as per proposed framework*
CRB – Proposed Upper Bound (7.5%) 1,06,696 1,14,153 1,23,806 95,171 70,477 44,862
CRB – Proposed Lower Bound (4.5%) (-) 53,348 (-) 57,077 (-) 61,903 (-) 95,171 (-) 1,40,954 (-) 1,83,901
Market risk ES 97.5% CL 0 72,296 1,03,886 0 0 0
Market risk ES 99.5% CL 0 3,08,563 3,50,032 1,09,174 2,07,352 0
Cumulative additional risk provisioning considering Market Risk Resilience at ES 97.5%*
CRB – Proposed Upper Bound 1,06,696 1,86,449 2,27,692 95,171 70,477 44,862
CRB – Proposed Lower Bound (-) 53,348 15,219 41,983 (-) 95,171 (-) 1,40,954 (-) 1,83,901
Cumulative additional risk provisioning considering Market Risk Resilience at ES 99.5%*
CRB – Proposed Upper Bound 1,06,696 4,22,716 4,73,838 2,04,345 2,77,830 44,862
CRB – Proposed Lower Bound (-) 53,348 2,51,486 2,88,129 14,002 66,398 (-) 1,83,901
* Risk provisioning over and above the provisions already maintained

1 Extant Economic Capital Framework

1.1 The extant Economic Capital Framework (ECF) was adopted by the Reserve Bank of India in August 2019, subsequent to the approval and acceptance of the recommendations of the ‘Expert Committee to Review the Extant Economic Capital Framework of the Reserve Bank of India’ (Chairman: Dr Bimal Jalan) by the RBI Central Board in its 578th meeting held on August 26, 2019. The ECF defines a risk-based economic capital benchmark for the RBI, which provides guidance on risk assessment methodologies, risk provisioning and surplus distribution, keeping in mind the statutory mandate under Section 475 of the RBI Act and the public policy mandate of RBI, along with the international best practices.

1.2 The Expert Committee had recommended that the framework may be periodically reviewed every five years (Para 4.98). Accordingly, a review of the Framework has been carried out. The succeeding section outlines the guiding principles underlying the current ECF as well as its salient aspects.

The extant Economic Capital Framework

1.3 The extant ECF of RBI is guided by the principle that the alignment of the objectives of the Government and the RBI is important. As the central bank is a part of the Sovereign, ensuring the credibility of the RBI is as important, if not more, to the Government, as it is to the RBI itself. The ECF also recognises the fact that being a public policy institution, RBI’s focus is on ensuring efficacy of its policy actions, even if such actions entail assuming significant balance sheet risks. Being the primary bulwark for monetary, financial and external stability, RBI’s financial resilience must be commensurate with the statutory responsibilities enshrined upon it, to ensure that RBI is seen as having the financial wherewithal to carry out loss-making policy actions, thereby ensuring their credibility. Box 1.1 outlines the salient aspects of extant ECF.

Box 1.1: The ECF of RBI – Salient Aspects

The ECF is an integral part of the Enterprise-wide Risk Management (ERM) framework which is being implemented in the Bank since 2012. The ECF follows from and is dovetailed with RBI’s Risk Tolerance Statement which, inter alia, states that financial risk considerations remain subordinate to the Bank’s public policy objectives, thereby necessitating the maintenance of adequate provisions in the form of economic capital to absorb risks that may materialise from any eventuality. It also recognizes that a failure to effectively manage risks may have an adverse impact on the achievement of RBI’s core objectives.

Assessment of risks under the ECF

To cover the entire gamut of risks facing RBI, the ECF stipulates prudent levels of economic capital to be maintained for market risk, credit risk, operational risk, and monetary and financial stability risk, which are assessed as per the following methodologies.

  1. The requirement of economic capital for market risk is assessed using Expected Shortfall (ES) under stressed conditions, with the target resilience determined at a confidence level (CL) of 99.5 per cent, and a lower tolerance threshold (risk tolerance limit, RTL) of 97.5 per cent CL.

  2. The provisioning for monetary and financial stability risks is based on scenario analysis, and is stipulated to be between 4.5 per cent and 5.5 per cent of the B/S size.

  3. The provisioning for credit risk (including for off balance sheet exposures) and operational risk is maintained at an implicit level of 1 per cent of the B/S size.

Components of economic capital under the ECF

  • Realized Equity and Revaluation Balances are the twin components of economic capital under the ECF, with the former largely comprising of realized risk provisions and the latter being the net valuation gains/ losses arising from periodic mark to market (MTM) of foreign currency assets, gold, domestic securities and forward contracts.

  • Realized Equity consists of RBI’s Capital, Reserve Fund, Contingency Fund (CF) and Asset Development Fund (ADF).

  • Revaluation Balances comprise of Currency and Gold Revaluation Account (CGRA), Investment Revaluation Account – Foreign Securities (IRA-FS), Investment Revaluation Account – Rupee Securities (IRA-RS) and Foreign Exchange Forward Contract Valuation Account (FCVA).

Applicability of risk buffers to risk exposures

The ECF establishes the principle of one-way fungibility wherein, revaluation balances can provide only for market risk, while realized equity can provide not only for monetary and financial stability risk, credit risk and operational risk, but also for residual market risk in case of shortfall in revaluation balances vis-à-vis the RTL requirement assessed using ES 97.5 per cent CL (stressed conditions).

Requirement of Realized Equity (RRE) and Surplus Distribution Policy

  • Revaluation Balances, being unrealized gains, are non-distributable.

  • Requirement of Realized Equity (RRE) stipulated to be at 6.5 per cent of the balance sheet, with a lower bound of 5.5 per cent, plus the shortfall in revaluation balances vis-à-vis their RTL requirement.

  • Available Realized Equity (ARE) to be compared with RRE, and risk provisioning to augment ARE to the level of resilience decided by the Central Board may first be carried out, with the residual net income being transferred to the Government.

2 Global macroeconomic environment and its impact on central bank balance sheets

2.1 An analysis of the ECF and its impact on RBI’s balance sheet must be seen in the context of the broader macroeconomic environment in which RBI has operated over the preceding five-year period. The succeeding sections provide a brief overview of the global macroeconomic environment and its impact on central banks’ profitability and balance sheet.

I. Global macroeconomic environment and its impact on central bank balance sheets

2.2 The previous five years have seen a period of extremely challenging macroeconomic environment, both on the global and domestic fronts. The period has been marked by several stress events, such as widespread disruptions to the economy and total output owing to the once-in-a-century pandemic, elevated global public debt on account of the pandemic-era expansionary fiscal policies, persistent inflation in the wake of supply chain disruptions, stretched asset valuations amid unprecedented volatility in financial markets, which have been aggravated by prolonged geopolitical tensions and geo-economic fragmentation.

2.3 Central banks adopted accommodative monetary policies as a response to the COVID-19 pandemic, leading to an inordinate expansion in their balance sheets (with concomitant increase in balance sheet risks), followed by aggressive and rapid monetary tightening in the face of persistent inflation.

2.4 The previous few years have seen many central banks reporting losses on an unprecedented scale, primarily on account of the twin-fold materialisation of interest rate risk. One, advanced economy central banks resorted to large-scale asset purchases as a part of quantitative and qualitative easing to maintain adequate liquidity in the financial system and support transmission of monetary policy. The purchase of these long-term fixed coupon assets was funded by creation of short-term reserves, resulting in an asset liability maturity mismatch, prone to repricing risk. As short-term interest rates rose rapidly on account of subsequent monetary tightening by central banks to rein in inflation, the significant increase in interest expense contracted the net interest margin, eventually resulting in a negative net interest income6, 7. Two, central banks with fair value accounting also suffered valuation losses on their portfolio of domestic and foreign securities, as interest rates rose. The impact of these valuation losses on central banks’ profitability was more pronounced in the case of central banks following IFRS 9 accounting standards, wherein valuation gains/ losses are taken to the P&L8, instead of being recorded in the balance sheet (as is the practice at RBI).

2.5 The economic capital frameworks, together with the surplus distribution policy of select Central Banks is presented at Annex I. An assessment of the impact of the macroeconomic environment on central banks’ profitability and equity is presented at Annex II.

II. Overview of recent literature on central banks’ capital adequacy

2.6 An overview of the literature suggests the presence of varied views on the role of central bank’s capital, with the case against adequate capital being centred on the ability of central banks to perform their domestic operations regardless of their net worth, as they can issue liabilities (‘print money’), and the fact that as central banks are a part of the government, it is the broader government balance sheet that matters (Anand et al.). A few authors have argued that a central bank’s balance sheet and financial strength do not necessarily have a significant link with inflation (Benecká, S et al.) or its ability to act as an effective Lender of Last Resort (LOLR) and Market Maker of Last Resort (MMLR) (Buiter et al.). However, the case against adequate capital is seen to suffer from a few limitations (Jamie Long et al.), such as, the potential inflationary impact of printing money to meet liabilities denominated in domestic currency, the explicit or implicit constraining of policy choices, the adverse perception of market participants with respect to policy independence and efficacy, and the strain on public finances and central bank independence in the event of a recapitalisation. Further, an overwhelming amount of literature makes a strong case for central banks with a sound capital base being able to deliver better on their policies, as financial strength can support central bank independence and credibility, particularly in signalling to the market that they are ready and able to act swiftly, and without constraint, in response to a crisis. Further, there is a view that central banks, who are also prudential regulators and supervise capital requirements of commercial banks, are better placed to do so if their institution is seen to be financially sound.

2.7 Financial independence, which inter alia includes the availability of a Reserve Fund, the ability to control distributions to the Government and exclude unrealized gains from net profit, has been assessed as the most critical metric (among ten metrics) for central bank autonomy in a survey involving 87 central banks by IMF for development of the Central Bank Independence Index (Tobias Adrian et al.). In fact, central banks of advanced economies (Klaas Knot et al.), despite being issuers of reserve currencies and being subjected to lesser risks from external spill-overs, have recognised the need for maintaining optimal capital and provisions ‘to maintain resilience, to absorb unexpected losses, to adapt to evolving risks, and to effectively fulfil mandates, even in challenging economic conditions’ as well as to maintain ‘public trust in central bank independence’, while noting that capital adequacy should take ‘jurisdiction-specific circumstances into account, as central banks have diverse mandates, operations and sizes’, an approach that is recognised by the ECF as well.

III. Scale of balance sheet challenges faced by RBI

2.8 During the Covid 19 pandemic, the policy toolkit adopted by RBI to ensure orderly conditions in the financial markets and transmission of monetary stability, saw the Bank undertake measures such as special liquidity facilities to ease redemption pressure on mutual funds and long-term lending, including targeted lending operations, to ensure that adequate liquidity is channelised to small and mid-sized corporates, microfinance institutions and non-banking financial companies.

2.9 As a result of the aforesaid liquidity extended by RBI along with an increase in CGRA on account of rise in foreign exchange reserves (due to robust capital inflows and cross currency movements) and depreciation in the rupee, the RBI’s balance sheet expanded by 30.02% in FY 2019-20 and at a Compounded Annual Growth Rate (CAGR) of 20.76% during the period June 30, 2019 to March 31, 2021. The aforesaid rate of expansion was much higher than that observed over the preceding 10 years (CAGR of 11.29%) and that projected by the Expert Committee in 2018-19, resulting in increased realized risk provisioning from net income. Further, the subsequent hardening of yields in both foreign and domestic securities, especially during the years 2021-2023, resulted in a decline in IRA balances of almost ₹3.32 lakh crore (equivalent to 5.24% of balance sheet as on March 31, 2023). Incidentally, a subset of the aforesaid period also saw materialization of exchange rate risk, with a decrease in CGRA being observed, partially because of rupee appreciation vis-à-vis the EUR and GBP. The movement in market risk factors over the previous five-year period is depicted in Chart 1.

Chart 1: Movement in risk factors (zero yields and exchange rates)

3 Review of the extant Economic Capital Framework

3.1 Despite the adverse macroeconomic developments and movement in risk factors during the review period, a combination of prudent accounting policies, ECF guidelines on provisioning requirements, and a rule-based Surplus Distribution Policy has ensured that RBI’s net income and economic capital levels remain resilient.

  1. Net valuation (unrealised) gains/ losses are recorded under Revaluation Accounts in the balance sheet and not included in the Income Statement, ensuring that the net income of RBI is not subject to volatile swings.

  2. Net unrealized losses in Revaluation Accounts are charged to CF on the date of finalisation of Annual Accounts, ensuring that these losses are fully provided for.

  3. Net income is first used for risk provisioning to augment Realized Equity to the resilience level decided by the Central Board, with only the residual net income being transferred to the Government. This has ensured that RBI’s economic capital has remained robust and its balance sheet resilient to risks.

3.2 The robustness of the ECF is evidenced by the fact that RBI has been able to not only maintain its financial resilience but also augment it, at a time when many central banks have reported net losses, and a few have completely depleted their equity. Moreover, RBI has also ensured healthy transfer of surplus to the Government, unlike many central banks, which have had to suspend transfer of surplus to their governments. Several central banks have projected that they may not be in a position to transfer any surplus to their Governments in the ensuing years, as the entire net income/ profit shall have to be retained to recoup the accumulated losses and restore the equity to a targeted level by building buffers.

3.3 Besides, the adoption and implementation of a rule-based, publicly disclosed economic capital framework on a consistent basis has helped build stakeholder confidence and trust in the commitment towards maintaining financial resilience of the Bank. The transparent approach has helped ensure that there are no concerns of arbitrariness in decisions concerning levels of risk provisioning and surplus transfer.

3.4 The evolution of RBI’s total economic capital along with the constituents of realized equity9 and revaluation balances10 during the last 10 years is given in Chart 2 below. It is seen that while revaluation balances (in rupee terms) have broadly followed an increasing trend, revaluation balances as a percentage of balance sheet size have largely followed a cyclical trend with a downward bias, which has been marked by lower highs and lower lows, especially during the last five years. Chart 3 depicts the improved composition of economic capital during the previous five-year period, with realized equity constituting a higher proportion of economic capital as on March 31, 2024, compared to that on June 30, 2019.

Chart 2: Components of Economic Capital (Last 10 years)

Chart 3: Composition of RBI’s Economic Capital

Risk provisioning and surplus transfer to Government (FY 2018-19 to 2023-24)

3.5 Over the period of operationalisation of extant ECF, RBI has, on an average, carried out risk provisioning equivalent to 36.68% of net income, while transferring 63.32% of net income to the Government. Though the average proportion of risk provisioning to net income has been higher than the preceding five-year period, which includes the period of operationalisation of the Malegam Committee recommendations (9.96% of net income) and that projected by the Expert Committee11, the same has been on account of the then unforeseen developments on the domestic and global macroeconomic fronts, including the pandemic and volatility in global financial markets. The segmentation of net income into risk provisioning and surplus transferred to the Government during the last 10 years is summarised in Table 1 and Chart 4 below.

Table 1: Risk Provisioning and Surplus Transferred – Last 10 years (in ₹ crore)
(Figures in parenthesis represent values as a percentage of net income)
Period Risk Provisioning Surplus Transferred Net Income^
2013-14 to 2017-18 29,330
(9.96%)
2,65,110
(90.03%)
2,94,460
(100.00%)
2018-19 to 2023-2412 3,82,752
(36.68%)
6,60,835
(63.32%)
10,43,611
(100.00%)
^Includes transfer of ₹1 crore each to four Statutory Funds, apart from risk provisioning and surplus transferred to Government.

Chart 4: Risk Provisioning and Surplus transfered

4 Review of Economic Capital Framework – Recommendations

4.1 The ECF has proven to be robust as it has met its objective of ensuring a resilient balance sheet for RBI, through many historic volatile episodes: (i) the once-in-a-century pandemic, that had a deep negative impact on economic growth and financial markets, not just in India but in every country in the world; (ii) major geo-political disruption, and the sanctions regime that followed as a response, which together are redrawing the contours of global supply chains and capital flows; (iii) the sharpest interest rate tightening by global central banks that the world has seen since the early 1980s, which particularly hurt central bank balance sheets; and (iv) two phases of sharp depreciation of EME currencies, including Indian Rupee. Not only has RBI’s balance sheet came out stronger from these negative episodes, RBI has managed to sustain and actually enhance surplus transfer to the Government during these five years. Therefore, it was felt judicious to continue with the same framework for economic capital recommended by the Expert Committee (Chair: Dr. Bimal Jalan), and adopted by the RBI in the preceding five years, as it has stood the test of extreme adversity.

4.2 Also, since the extant methodologies for market, credit and operational risks are based on global standards, the review proposes no major changes in risk assessment methodologies and the assumptions underlying them, as the same were recommended by the Expert Committee after comprehensive evaluation of available risk methodologies and the appropriateness of their applicability in RBI’s case.

4.3 At the same time, the review considered the experience gained from the operationalization of the current ECF over the last five years, the changes in the asset profile of the Bank’s balance sheet, and the developments in the domestic and global operating environment. Based on the above it highlighted two areas where the framework could be further bolstered to ensure continued alignment with its core objective, as follows:

  1. The transfer of surplus to the Government has not been as stable as was desirable, as explained in para 4.25 further down in this chapter. Therefore, it was considered desirable to afford the Central Board more flexibility to smoothen the transfer of surplus over the years.

  2. While the framework itself has proven to be robust, certain risk sources (e.g., market risk on off-balance sheet exposures) that were not included in the current framework since they were not significant, have now gained in importance and merit inclusion.

The recommendations made by the review are presented below.

Components of RBI’s economic capital

4.4 Realized Equity and Revaluation Balances may continue to be the twin components of RBI’s economic capital, with the extant principle of one-way fungibility (implying that revaluation balances cannot provide for risks other than market risk, while realized equity can provide for all risks, including market risk) continuing to be applicable.

I. Risk parameterisation and Provisioning for market risk

4.5 An analysis of the methodologies used to assess and quantify market risks brings out the fact that Expected Shortfall (ES) continues to be the gold standard. As such, it is proposed that the assessment of market risk buffer requirement for on-balance sheet items may continue to be carried out using a parametric distribution of returns and applying the Expected Shortfall model under stressed conditions.

4.6 The Expert Committee had noted in its report that RBI should put in place a framework for assessing the market risk of its off-balance sheet (OFBS) exposures in view of their increasing significance (Para 4.50). Accordingly, it is proposed that an integrated approach may be adopted, wherein the OFBS portfolio13 is also considered, together with the on-B/S portfolio, while computing the market risk buffer requirement.

4.7 With respect to the choice of reference period, a simulation exercise was carried out to determine the most stressful period for computation of variance-covariance matrix, as part of computation of ES. It is observed that the period ended August 2013 (which was adopted by the extant ECF) continues to be the most appropriate for computation of stress variance-covariance matrix. However, it is proposed that the variance covariance matrix of price returns (which is used for computation of ES) may, henceforth, be computed directly from price returns, instead of the existing process of approximating it by transforming the variance-covariance matrix of yield returns using pre- and post-multiplicative factors. The proposed method would be statistically sound and is observed to result in a marginal increase in the requirement for market risk buffers. This is also followed for management of foreign exchange reserves by the Bank. The other parameters used in the computation of ES were reviewed to ensure their appropriateness, and it is proposed that they may continue to remain the same. The rationale for computing variance-covariance matrix directly from price returns is provided in Annex III.

4.8 The computation of economic capital currently considers only the major currencies in which forex reserves are deployed, along with gold, while computing the requirement for market risk buffers. In this regard, it is proposed that going forward, the requirement of market risk buffers may also consider the deployment of Foreign Currency Assets in minor currencies.

4.9 With regard to the confidence levels (CLs) to be chosen for maintenance of market risk buffers, a review of the existing parameters was carried out in terms of their adequacy under various stress scenarios. Under the extant ECF, the CL of 97.5 per cent was chosen so as to provide adequate protection against a 20 per cent appreciation of Rupee vis-à-vis the USD and 300 bps jump in domestic yields. The CL of 99.5 per cent provided additional (though limited) protection (up to 3.6 per cent of balance sheet) against cross-currency risk, gold price risk, yield risk in foreign securities and forward contracts valuation risks. A similar exercise was carried out as part of the review by considering various scenarios, including the scenario indicated above. Table 2 below illustrates the impact of adverse movement in exchange rates and yield curves on market risk buffers at ES 99.5 per cent (stress) and 97.5 per cent CL (stress), respectively.

Table 2: Impact of movement in exchange rates and yields on market risk buffers
(₹ crore) (% of BS) (portfolio as on March 31, 2024)
  Domestic yield jump Foreign yield jump Cross Currency depreciation wrt. USD INR appreciation wrt. USD Expected MTM loss Residual balance wrt ES 99.5% Residual balance wrt ES 97.5%
Scenario A 300 bps Nil Nil 20% 10,28,750 2,60,203
(3.69%)
13,218
(0.19%)
Scenario B 150 bps 150 bps Nil 20% 11,11,562 1,77,391
(2.52%)
-69,594
(-0.99%)
Scenario C 200 bps 200 bps 5% 15% 11,24,390 1,64,563
(2.33%)
-82,422
(-1.17%)
Scenario D 100 bps 100 bps 5% 20% 11,26,293 1,62,660
(2.31%)
-84,325
(-1.20%)
Scenario E 300 bps 100 bps Nil 20% 11,42,310 1,46,643
(2.08%)
-1,00,342
(-1.42%)
Scenario F 200 bps 200 bps 5% 20% 12,95,874 -6,921
(-0.10%)
-2,53,906
(-3.60%)
Scenario G 150 bps Customised* 15% 10% 13,13,507 -24,554
(-0.35%)
-2,71,539
(-3.85%)
Scenario H 150 bps Customised** 15% 10% 14,06,671 -1,17,718
(-1.67%)
-3,64,703
(-5.17%)
*A yield jump of 375, 300, 325, 525, 50, 250 and 375 bps has been considered for the portfolio of dated securities denominated in AUD, CAD, EUR, GBP, JPY, NOK and USD respectively, in line with the maximum yield increase observed over a 1-year period during the recent spell of monetary tightening.
** A yield jump of 400, 400, 375, 575, 50, 350 and 475 bps has been considered for the portfolio of dated securities denominated in AUD, CAD, EUR, GBP, JPY, NOK and USD respectively, in line with the maximum yield increase observed during the recent spell of monetary tightening beginning Aug 2021.

4.10 Scenario A assumes shocks similar to those assumed by the extant ECF. Under this, market risk buffers equivalent to ES (stress) 99.5 per cent CL leave a residual buffer of 3.69 per cent of BS size for covering the excluded risks, while market risk buffers equivalent to ES (stress) 97.5 per cent CL are only adequate to meet the assumed shocks. Market risk buffers at ES (stress) 97.5 per cent CL fail to provide adequate protection against adverse movements in risk factors under all other scenarios, while buffers at ES (stress) 99.5 per cent CL provide adequate protection under all scenarios, except Scenarios F, G and H. In view of the inadequacy of buffers at ES (stress) 97.5 per cent CL to provide optimal level of protection to the balance sheet under certain scenarios, it is proposed to introduce flexibility14 to consider additional risk provisioning from Realized Equity/ Net Income (at the time of finalization of Annual Accounts), to augment market risk buffers to the level of resilience decided by the Central Board, within a range of ES at 99.5% CL and ES at 97.5% CL. The aforesaid flexibility to the Central Board would not only help ensure optimal resilience for RBI’s balance sheet to persistent adverse movement in risk factors but also offer the necessary flexibility to see through their transient movements.

4.11 Revaluation balances in excess of their requirement, if any, shall, by virtue of being unrealized gains, continue to be on the B/S for meeting market risks and will not be available for distribution. The impact of the proposals on the requirement of risk buffers for market risk over the last five years is detailed in Annex IV.

II. Provisioning for credit risk and operational risk

4.12 The assessment of economic capital requirement for credit risk (including on account of OFBS exposures) and operational risk may continue to be carried out as hitherto. The requirement for economic capital, assessed as above, has remained around one per cent of the B/S size. Accordingly, it is proposed that the implicit combined requirement of realized equity for credit risk and operational risk at one per cent of B/S size may continue to be maintained, in line with the extant framework.

III. Provisioning for monetary and financial stability risks

4.13 The ECF recognizes financial stability risks as the rarest of rare fat tail risks, the occurrence of which can potentially devastate the economy, and the concomitant responsibility on central banks, including RBI, to safeguard financial system stability. This may include measures such as providing emergency liquidity assistance, even by diluting collateral standards, and undertaking asset purchases, including private ones, to address market dysfunction and support monetary policy objectives, even if it entails assuming significant credit risk. In recognition of the fact that RBI forms the primary bulwark for monetary and financial stability, the Expert Committee had recommended that the size of the monetary and financial stability risk provisions be maintained between 4.5 to 5.5 per cent of the balance sheet size, to ensure the availability of adequate financial resources to assuage market participants’ concerns in case of a systemic stability crisis, and for the RBI’s crisis mitigating measures to be seen as credible.

4.14. The size of the monetary and financial stability risk provision was arrived at by the Expert Committee, with a view to ensure that potential losses arising on account of providing emergency liquidity assistance (ELA) to Top 10 SCBs in the event of a relatively adverse liquidity shock, are completely provided for. Though the ELA provided by RBI is expected to be collateralized, the ELA extended to SCBs beyond their stock of High-Quality Liquid Assets (HQLA), exposes RBI not only to market risk, but also credit risk. In view of this, the monetary and financial stability risk provisions have been maintained with RBI as the country’s savings for a rainy day, in view of its role as the LOLR. The review assesses the recent and emerging global macroeconomic factors that may impact monetary and financial stability, while also taking into account the resilience demonstrated by the Bank and the banking system over the past five years, which are discussed in the subsequent paras.

4.15 During the pandemic, central banks resorted to unconventional and riskier policy tools to restore monetary and financial system stability, such as engaging in large scale asset purchases. The likelihood of central banks having to resort to unconventional monetary policy tools in periods of future crises can also be gauged from the fact that central banks of many small open economies (SOEs) and emerging market economies (EMEs) launched asset purchase programs for the first time in response to the Covid-19 crisis, along with an expanded implementation by Advanced Economy (AE) central banks15. It is also observed that the range of assets covered by central banks’ purchase programmes was wider, and credit quality lower, than in the past, with several EME central banks purchasing private assets for the first time.

4.16 Though unconventional monetary policy tools have had a stabilising impact on financial markets, with a reduction in liquidity, credit risk and term premia, they also led to an increase in central banks’ exposure to risks by transferring risks from the private sector to the public sector. In the case of India, though the purchase of assets post-pandemic was confined to public assets, the possibility of private asset purchases in future periods of crisis may not be ruled out. Similarly, the possibility of providing direct liquidity assistance to AIFIs, NBFCs, MFIs, corporates and mutual funds against non-HQLA collateral during a future crisis, may also not be ruled out, especially if the risk appetite of the banking system is low or its capital position is strained.

4.17 With regard to other sources of contingent financial stability risks, the interconnectedness between banks and non-bank financial entities in the financial system is seen to be increasing, thereby increasing the risk of a contagion in a financial crisis. Further, given the global operations of SCBs, the possibility of RBI having to provide liquidity in foreign currency to overseas branches of SCBs in periods of stress, with tightening of counterparty credit lines and widening of spreads, may not be ruled out.

4.18 However, it is pertinent to also highlight the resilience demonstrated by the Bank in the face of the extreme macroeconomic factors, as elaborated in Chapter 2 of the report. This resilience of the Bank is of importance in the broader context of monetary and financial stability, especially when numerous other central banks have incurred losses and have depleted their equity in the preceding five years in their efforts to maintain monetary and financial stability. The resilience of the Bank’s Balance Sheet, assessed in terms of economic capital, risk provisions and surplus transfer to the Government, is elaborated in Chapter 3 of the report.

4.19 The resilience is also significant as it persisted despite the Bank undertaking several targeted measures during the pandemic to support the financial system and stabilise the broader economy. While these measures had the potential to impact the Balance Sheet, the Bank did not experience any such adverse outcomes. This indicates the strength and resilience of the Balance Sheet of the Bank, even during macroeconomic volatility and systemic stress.

4.20 In recent years, the foreign exchange reserves of the Bank have increased significantly from USD 433.71 billion as at end-September 2019 to USD 665.40 billion as at end-March 2025. The accretion has enhanced the Bank’s capacity to manage external shocks, mitigate exchange rate volatility, and thereby support monetary and financial stability, besides improving the resilience of its balance sheet.

4.21 Besides the resilience of the central bank, the banking sector has also exhibited a sharp improvement in the asset quality, indicating more resilient balance sheets and a lower risk of financial instability. The latest Financial Stability Report also reaffirms the resilience of the balance sheet of banks, by highlighting that the gross non-performing assets (GNPA) ratio of SCBs fell to a multi-year low of 2.6 per cent, buoyed, inter alia, by falling slippages.

4.22 In view of the lessons learnt from the cross-country experience of central banks as well as the ongoing uncertainty arising from spill-over effects of macroeconomic and geopolitical developments, the need for RBI to maintain an optimal level of realized equity to credibly discharge its mandate of safeguarding the monetary, financial and external stability of the country has been amplified. However, the resilience demonstrated by the Bank in recent years, despite the pandemic and its aftermath, reinforces the Bank’s ability to manage monetary and financial stability risks effectively and underscores the strength of its balance sheet.

4.23 While it was felt that the scenario of the top 10 SCBs experiencing liquidity stress simultaneously is rather conservative considering that the share of deposits of these 10 banks account for more than 74.75% of the deposits of all the SCBs, it was nonetheless decided to adhere to the basic structure of the assessment carried out by the Expert Committee. Accordingly, an assessment of the ELA requirement of Top 10 SCBs was carried out for position as on March 31, 2025. During the review, it was noted that the asset quality of the banking system had substantially improved since the assessment by the Expert Committee, as evident from the drop in GNPA ratios of SCBs from 9.3% (12.6% for PSBs) in March 2019 to a multi-year low of 2.6% (3.3% for PSBs) in September 2024. Although the Expert Committee had estimated potential LOLR losses for RBI based on uniform recovery rate of 80 per cent on ELA against non-HQLA collateral for both private and public sector banks, the present review proposes to account for the inherent strength due to sovereign ownership in case of PSBs, while assessing the recovery rates. This was evidenced by the fact that the Government had infused an amount of more than ₹3,15,000 crore as capital during the period since RBI’s Asset Quality Review. Accordingly, the potential LOLR losses of RBI for the quantum of loans extended to PSBs have been assumed to be lower (10%) as compared to private sector banks (20%). In view of this, the current assessment, broadly consistent with the assumptions used by the Expert Committee, indicate the potential losses to RBI at 2.97 per cent of RBI’s balance sheet in case of a liquidity stress scenario involving the top 10 banks and recovery rate of 90% for PSBs and 80% for private sector banks. (Table 3).

Table 3: ELA to Top 10 SCBs - LOLR Losses as % of RBI BS size
Public Sector Banks Private Sector Banks Adverse Shock Scenario Extreme Shock Scenario
90% Recovery rate 80% Recovery rate 2.97% 5.98%
80% Recovery rate 60% Recovery rate 5.94% 11.97%

4.24 However, the analysis did not take into consideration other potential sources of monetary and financial stability risks listed earlier, which may also be considered while determining the applicable range for monetary and financial stability risks.

4.25 It is also seen that the period of the extant ECF has seen considerable volatility in the transfer of surplus to the Government, as indicated by a Coefficient of Variation (CV) of 63.30 per cent16. It is observed that the existing range of 1.0 per cent for buffers for monetary and financial stability risks provides very limited flexibility to the Central Board to smoothen the transfer of surplus to the Government. As surplus generated is essentially a function of the cyclical interest rates, a case could be made for a wider range, which will provide adequate flexibility to the Central Board to smoothen transfer of surplus to the Government.

4.26 In view of the above factors, it is proposed to change the buffer requirement for monetary and financial stability risks. Currently, these buffers are maintained at the resilience level decided by the Central Board, subject to a range of 4.5 per cent to 5.5 per cent of B/S size. It is proposed to widen the applicable range for buffer requirement for monetary and financial stability risks to 5.0 ± 1.5 per cent. The wider range would provide adequate flexibility to the Central Board in determining the buffers, taking into account the prevailing macroeconomic and other factors, while also smoothening the transfer of surplus to the Government. The range also provides adequate headroom vis-à-vis the potential LOLR loss at 2.97% of B/S size.

Requirement of Realized Equity

4.27 The Requirement of Realized Equity (RRE) is to be assessed as the size of realized equity to meet the requirement for Contingent Risk Buffer (CRB) and shortfall, if any, in revaluation balances vis-à-vis the requirement for market risk buffers at the Central Board-determined resilience level. The CRB shall provide for monetary and financial stability risks, credit risk, and operational risk, as per the requirements specified in previous paras, and would be maintained within the range of 6.0 ± 1.5 per cent of the Balance Sheet size (as against the level of 6.5 per cent, with lower bound of 5.5 per cent of B/S size under the extant ECF). The upper bound of RRE would be based on CRB computed assuming buffers for monetary and financial stability risks at their upper bound of 6.5 per cent of B/S size, while the lower bound of RRE would be based on CRB computed assuming buffers for monetary and financial stability risks at their lower bound of 3.5 per cent of B/S size.

IV. Surplus Distribution Policy (SDP)

4.28 The SDP shall continue to treat revaluation balances as non-distributable, while imparting primacy to bolstering RBI’s financial resilience to the desired level, with only the residual net income being available for transfer to the Government. The SDP shall compare the Available Realized Equity (ARE) (comprising Capital, Reserve Fund, CF and ADF) with its requirement (RRE), and allocate net income in the following manner:

  1. Entire net income may be transferred to the Government if the RBI’s ARE is equal to or greater than the upper bound of the RRE.

  2. In case the ARE lies within the upper bound and lower bound of RRE, the Central Board may determine the level at which ARE may be maintained (subject to the upper bound and lower bound of RRE) and accordingly, risk provisioning and surplus distribution may be carried out.

  3. If the ARE falls short of lower bound of RRE, appropriate risk provisioning may be made by the RBI to augment ARE to at least its lower bound and only the residual net income (if any) may be transferred to the Government. If net income is lower than the risk provisioning required to augment the ARE to the lower bound of RRE, no surplus may be transferred to the Government and the Bank may endeavour to augment its ARE to at least the lower bound of RRE in the subsequent year(s), prior to resuming transfer of surplus to the Government.

  4. There shall be no distribution of unrealized revaluation balances.

  5. The excess realized equity, i.e., ARE in excess of RRE, shall be written back from the Contingency Fund (CF) to income at the time of finalization of Annual Accounts.

V. Impact of Recommendations

4.29 A comparative analysis of risk provisioning, under the extant ECF and proposed ECF, considering the buffers for monetary and financial stability risks being maintained within the proposed range of 5.0 ± 1.5 per cent of the B/S size, is summarized in Table 4 below:

Table 4: EC Requirement under extant and revised ECF – March 31, 2025 - % of B/S (₹ cr)
S.No. Risk type Parameter Requirement of EC Available EC Risk provisioning
Extant Proposed Extant Proposed
Ia. Market risk (On BS items) 99.5% CL 17.86% 18.91% 17.40% (RB)    
97.5% CL 14.44% 15.29%
Ib. Market risk (OFBS) 99.5% CL NA -2.19%
97.5% CL NA -1.77%    
I.
(Ia+Ib)
Market risk (total) 99.5% CL 17.86% 16.72% NA 0%
97.5% CL 14.44% 13.51% 0% 0%
II. Credit & op risk Extant 1% 1% 6.91% (RE)    
III. Monetary and financial stability risk Upper Bound 5.5% 6.5%
Lower Bound 4.5% 3.5%
IV.
(II+III)
Total non-valuation risks Upper Bound 6.5% 7.5% (-) 0.41%
₹ (-) 31,393
0.59%
₹ 44,862
Lower Bound 5.5% 4.5% (-) 1.41%
₹ (-) 1,07,647
(-) 2.41%
₹ (-) 1,83,901

Table 4 indicates that as on March 31, 2025, revaluation balances are adequate to meet market risk buffer requirements. Applying the range of 5.0 ± 1.5 per cent of the B/S size as buffer for monetary and financial stability risks, the RRE was in the range of 6.0 ± 1.5 per cent of the B/S size. As against this, the ARE stood at 6.91% of B/S size. Accordingly, at the upper bound of RRE i.e., 7.5%, additional provisioning of ₹44,862 crore would be required from Net Income, while at the lower bound of RRE i.e., 4.5%, an amount of ₹1,83,901 crore would be written back from CF to Income.

4.30 The impact of proposed recommendations on risk provisioning and surplus transferable over the last five years is placed in Annex V.

5 Summary of Recommendations

5.1 Risk parameterisation for market risk

• An integrated approach may be adopted, wherein the off-balance sheet portfolio is also considered, together with the on-B/S portfolio, while computing market risk buffer requirement.

(Para 4.6)

• Introduction of flexibility to the Central Board to maintain market risk buffers at any resilience level within a range of ES at 99.5 per cent CL and ES at 97.5 per cent CL.

(Para 4.10)

Enhancement in Methodology: The variance covariance matrix of price returns (which is used for computation of ES) may be computed directly from price returns, instead of the existing process of approximating it by transforming the variance-covariance matrix of yield returns using pre- and post-multiplicative factors.

(Para 4.7)

Inclusion of Minor Currencies: The requirement of market risk buffers may also consider the deployment of Foreign Currency Assets in minor currencies.

(Para 4.8)

5.2 Provisioning for credit risk and operational risk – Economic capital for credit risk (including on account of OFBS exposures) and operational risk may continue to be maintained as hitherto.

(Para 4.12)

5.3 Provisioning for monetary and financial stability risk – It is proposed to widen the applicable range for buffer requirement for monetary and financial stability risks to 5.0 ± 1.5% (vis-à-vis range of 4.5% - 5.5% under extant ECF), with the objective of providing adequate flexibility to the Central Board in determining the buffers, keeping in mind the prevailing macroeconomic and other factors, while also smoothening the transfer of surplus to the Government.

(Para 4.26)

5.4 Requirement of Realized Equity (RRE) – The Requirement of Realized Equity (RRE) is to be assessed as the size of realized equity to meet the requirement for Contingent Risk Buffer (CRB) and shortfall, if any, in revaluation balances vis-à-vis the requirement for market risk buffers at the Central Board-determined resilience level. The CRB shall provide for monetary and financial stability risks, credit risk and operational risk, and would be maintained within the range of 6.0 ± 1.5 per cent of the Balance Sheet size (as against the level of 6.5 per cent, with lower bound of 5.5 per cent under extant ECF). The upper bound of RRE would be based on CRB computed assuming buffers for monetary and financial stability risks at their upper bound of 6.5%, while the lower bound of RRE would be based on CRB computed assuming buffers for monetary and financial stability risks at their lower bound of 3.5%

(Para 4.27)

5.5 Available economic capital/ risk buffers – The Requirement of Realized Equity shall be met exclusively by the Available Realized Equity comprising the Bank’s Capital, Reserve Fund, Contingency Fund and Asset Development Fund. The extant principle of one-way fungibility (implying that revaluation balances cannot provide for risks other than market risk, while realized equity can provide for all risks, including market risk) would continue to be applicable to the twin components of RBI’s economic capital.

5.6 Surplus Distribution Policy (SDP)

The SDP shall continue to treat revaluation balances as non-distributable, while imparting primacy to bolstering RBI’s financial resilience to the desired level, with only the residual net income being available for transfer to the Government. The SDP shall compare the Available Realized Equity (ARE) (comprising Capital, Reserve Fund, CF and ADF) with its requirement (RRE), and allocate net income in the following manner:

  1. Entire net income may be transferred to the Government if the RBI’s ARE is equal to or greater than the upper bound of the RRE.

  2. In case the ARE lies within the upper bound and lower bound of RRE, the Central Board may determine the level at which ARE may be maintained (subject to the upper bound and lower bound) and accordingly, risk provisioning and surplus distribution may be carried out.

  3. If the ARE falls short of lower bound of RRE, appropriate risk provisioning may be made by the RBI to augment ARE to at least its lower bound and only the residual net income (if any) may be transferred to the Government. If net income is lower than the risk provisioning required to augment the ARE to the lower bound of RRE, no surplus may be transferred to the Government and the Bank may endeavour to augment its ARE to at least the lower bound of RRE in the subsequent year(s), prior to resuming transfer of surplus to the Government.

  4. There shall be no distribution of unrealized revaluation balances.

  5. The excess realized equity, i.e., ARE in excess of RRE, shall be written back from the Contingency Fund (CF) to income at the time of finalization of Annual Accounts.

(Para 4.28)


References

1. Economic & Political Weekly, 53, (48) (2018) – ‘Paranoia or Prudence? How Much Capital is Enough for the RBI?’ by Anand, Abhishek, Felman, Josh, Sharma, Navneeraj, & Subramanian, Arvind (2018).

2. Czech National Bank Working Papers, series 3 (2012) - ‘Does Central Bank Financial Strength Matter for Inflation? An Empirical Analysis’ by Benecká, S., Holub, T., Kadlčáková, N.L., & Kubicová I.

3. CEPR Discussion Papers No. 6827, London, Centre for Economic Policy Research (2008) - ‘Can Central Banks Go Broke?’ by Buiter, Willem H. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=6827.

4. Bank of England Staff Working Paper No. 1,069 (April 2024) – ‘Central bank profit distribution and recapitalisation’ by Jamie Long and Paul Fisher. Retrieved from Central bank profit distribution and recapitalisation | Bank of England

5. IMF Working Paper WP/24/35 (Feb 2024) - ‘A New Measure of Central Bank Independence’ by Tobias Adrian, Ashraf Khan and Lev Menand. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2024/02/23/A-New-Measure-of-Central-Bank-Independence-545270

6. Speech by Klaas Knot, President, De Nederlandsche Bank (April 2024) - ‘Central bank capital - of capital importance?’ Retrieved from https://www.bis.org/review/r240415d.htm


Annex I

Surplus Distribution Policy, Extant Economic Capital Frameworks and Accounting Standards of select Central Banks

Central Bank Surplus Transfer Policy
Bank of England The framework for the bank’s capital is in terms of the MoU17 between BoE and HM Treasury. The metric to which the capital framework applies is the loss-absorbing capital (LAC) of the Bank. The LAC is the Bank’s total capital less any capital components that cannot absorb losses. The parameters of the capital framework include a target, a floor, and a ceiling. The various scenarios envisaged are as under:
  • In case the LAC for the following period exceeds the target but below the ceiling, 50% of net profits is paid as the dividend to the Treasury.

  • If the metric exceeds the ceiling, 100% of net profits is paid as dividend to the Treasury.

  • If the metric is below the floor, the Bank receives a capital injection from the Treasury to return to target.

Reserve Bank of Australia Net profit is dealt with statutorily in the following manner18:
  • Unrealised gains (or losses) are not available for distribution and are transferred to (absorbed by) the unrealised profits reserve. The remainder of net profit after this transfer is available for distribution.

  • The Ministry determines, after consulting the Bank Board, any amounts to be placed from distributable earnings to the credit of the Reserve Bank Reserve Fund (RBRF), the Bank's general reserve and to be set aside for contingencies.

  • The remainder of distributable earnings is payable as a dividend to the Commonwealth.

The capital and statutory reserves are separate from the unrealised profit reserve.
Reserve Bank of New Zealand

In terms of the Act19, the Bank must determine the amount it recommends to the Government in accordance with the principles set out in the statement of financial risk management20. The statement, brought out annually, sets out the minimum level of capital (Target Capital Level or ‘TCL’) that is deemed sufficient to cover potential financial risks.

2. Financial loss modelling is completed on the most significant risks, and risk limits are set to ensure the Bank’s balance sheet remains resilient under severe but plausible stressed market conditions (based on historical experience).

Bank of Canada In terms of the Act21, the surplus available from the operations of the Bank during each financial year is to be determined in the following manner:
  • If the Bank’s reserve fund is less than the paid-up capital, one third of the surplus is to be allocated to the reserve fund and the remainder is to be paid to the Government.

  • If the reserve fund is not less than the paid-up capital, one fifth of the surplus is to be allocated to the reserve fund until the reserve fund reaches an amount five times the paid-up capital and the remainder is to be paid to the Government.

  • if the reserve fund is not less than five times the paid-up capital, the whole of the surplus is to be paid to the Government.

2. The Act22 also provides for the creation of a special reserve fund, wherein funds are allocated from the surplus, to offset unrealized valuation losses due to changes in the fair value of the investment portfolio of the Bank.
Swiss National Bank (SNB) In terms of the Act23, SNB is required to set up provisions permitting it to maintain the currency reserves at a level necessary for monetary policy. The remaining earnings are deemed to be distributable profit. Currently, the minimum annual allocation of percentage of profit to provisions is at 10%24.
Bank of Thailand (BoT) In terms of provisions of the Act25, 25% of net profits are retained as reserves. The Act has provided for other specific reserves to be retained, subject to approval.

2. Unrealized gains or losses from revaluation of assets and liabilities of the BOT as at the end of period are presented in the Assets and Liabilities Revaluation Reserve26 under the equity section.
Bank Negara Malaysia In terms of the Act27, the surplus is to be transferred, subject to the following conditions:
  • If the General Reserve Fund is less than the capital of the Bank, the whole of the net profit shall be credited to the General Reserve Fund

  • In the case of any year at the end of which the General Reserve Fund is not less than the capital of the Bank, but less than twice the capital of the Bank, not less than thirty per centum of the net profit shall be credited to the General Reserve Fund.

  • Any net profit not retained as reserves is to be transferred to the Government

2. The income in the P&L statement28 includes only the realised capital gains or losses. The unrealised gains/losses are part of the risk reserve and are not part of the general reserve.
Bank of Korea In terms of the Act29, the Bank is permitted to transfer 30% of the net profits earned for the year to its reserve. Additional net profit may be retained for transferring to a special reserve, on approval from Government. The remaining amount is to be transferred to the Government.
Philippines (Bangko Sentral ng Pilipinas) Currently, 50% of the Central Bank’s net profits are distributed to the Government as dividends. Unrealized gains or losses are recognized in the revaluation reserve account and are not accounted for in the P&L statement.30

Annex II

Profitability and equity of central banks – Impact of macroeconomic environment

Table A1 below presents a few examples of central banks who have experienced adverse profitability and equity position in recent years (FY 2020-21 to 2023-24), in pursuance of their mandates.

Table A1: Central banks with adverse impact on profitability and equity in recent years
Central Bank Negative net interest income Valuation loss resulting in net loss Total Negative Equity Zero/ negative realized equity*
Federal Reserve Bank    
Deutsche Bundesbank    
Swiss National Bank      
Bank of Canada    
Reserve Bank of Australia  
Czech National Bank  
Monetary Authority of Singapore  
Narodowy Bank Polski (Poland)  
Reserve Bank of New Zealand      
Sverges RiksBank      
* Except for paid-up capital and statutory reserves in certain cases.

Annex III

Rationale for computing variance-covariance matrix from price returns and impact of methodology changes to economic capital requirement for market risk

The existing MATLAB based market risk engine approximates variance covariance matrix of price returns of specified maturity buckets in various currencies by transforming the variance covariance matrix of yield returns using pre and post multiplicative factors while computing parametric VaR/ ES. The variance covariance matrix of price returns is required in order to compute the portfolio’s variance and consequently the VaR/ ES at specified CL. The pre and post multiplicative factor is a diagonal matrix arrived at as the product of modified duration and yield (on reference date) for the corresponding currency and maturity bucket.

2. While the above approximation is not otherwise seen to have a significant impact, an episode in 2021-2022 where EUR zero coupon yields were close to zero and transitioning from negative to positive, resulted in an unusually high value of computed variance on account of high values of yield returns (as denominator was close to zero), which was not being offset in the pre and post multiplicative factors, which considers a constant value of yield (prevailing on the reference date).

3. The aforesaid issue, essentially arising from usage of running yields in variance covariance matrix of yield returns compared to constant yield in pre and post multiplicative matrices, may be overcome if variance covariance matrix of price returns is computed directly by deriving price of zero-coupon bonds from zero coupon yields using continuous compounding.

4. Though the aforesaid issue did not have an impact on requirement of market risk buffers computed during the review period, as the reference date (stress period) under the approved ECF is August 30, 2013, adoption of the proposed method (of computing variance covariance matrix directly) would be statistically consistent and ensure that no undue anomaly is observed in assessment of market risk should the reference period change from August 2013 to 2022 and beyond. This is also followed for management of foreign exchange reserves by the Bank.


Annex IV

Comparison of risk buffers for market risk under proposed and extant ECF

The requirement of risk buffers for market risk as per the proposed recommendations vis-à-vis their requirement under the extant ECF for the previous five FYs, is placed in Table A2 below.

Table A2: Requirement of buffers for market risk (proposed vis-à-vis extant) (₹ crore)
  Jun 2020 Mar 2021 Mar 2022 Mar 2023 Mar 2024 Mar 2025
B/S size 53,34,793 57,07,669 61,90,302 63,44,756 70,47,703 76,25,422
Available RB 11,24,390 9,24,455 9,34,544 11,26,088 11,30,964 13,26,793
Additional impact on account of computation of VC matrix using price returns
ES 97.5% CL 8,585 11,705 10,201 16,547 23,577 23,960
ES 98.0% CL 8,890 12,121 10,565 17,136 24,415 24,811
ES 98.5% CL 9,271 12,640 11,018 17,870 25,461 25,875
ES 99.0% CL 9,787 13,344 11,631 18,865 26,879 27,315
ES 99.5% CL 10,619 14,479 12,620 20,469 29,165 29,639
Additional impact on account of inclusion of minor currencies
ES 97.5% CL 3,585 2,688 2,298 10,863 18,694 40,712
ES 98.0% CL 3,712 2,784 2,379 11,249 19,358 42,159
ES 98.5% CL 3,871 2,903 2,481 11,732 20,188 43,966
ES 99.0% CL 4,087 3,065 2,619 12,384 21,312 46,414
ES 99.5% CL 4,434 3,325 2,842 13,438 23,125 50,362
Additional impact on account of OFBS exposures
ES 97.5% CL 2,720 1,13,386 1,02,288 40,812 (2,366) (1,35,065)
ES 98.0% CL 2,816 1,17,416 1,05,924 42,262 (2,450) (1,39,867)
ES 98.5% CL 2,937 1,22,450 1,10,465 44,074 (2,555) (1,45,863)
ES 99.0% CL 3,100 1,29,266 1,16,614 46,527 (2,697) (1,53,981)
ES 99.5% CL 3,364 1,40,263 1,26,534 50,485 (2,927) (1,67,081)
Total requirement of buffers as per proposed recommendations
ES 97.5% CL 8,01,190 9,96,751 10,38,430 9,98,565 10,81,872 10,30,545
ES 98.0% CL 8,29,670 10,32,183 10,75,344 10,34,062 11,20,331 10,67,178
ES 98.5% CL 8,65,240 10,76,435 11,21,446 10,78,394 11,68,361 11,12,930
ES 99.0% CL 9,13,397 11,36,347 11,83,863 11,38,415 12,33,390 11,74,873
ES 99.5% CL 9,91,101 12,33,018 12,84,576 12,35,262 13,38,316 12,74,822
Difference in requirement of buffers (proposed vis-à-vis existing)
ES 97.5% -99.5% CL 14,889 to 18,418 1,27,779 to 1,58,067 1,14,787 to 1,41,996 68,222 to 84,393 39,904 to 49,363 (-) 87,080 to (-) 70,394
ES 97.5% -99.5% CL 0.28% to 0.35% 2.24% to 2.77% 1.85% to 2.29% 1.08% to 1.33% 0.57% to 0.70% (-) 1.14% to (-) 0.92%

Annex V

Impact of proposed recommendations on risk provisioning and surplus transferable

The impact of proposed recommendations on risk provisioning and surplus transferable, considering the buffers for monetary and financial stability risks being maintained within the proposed range of 5.0 ± 1.5 per cent of the B/S size, is placed in Table A3 below.

Table A3: Impact of proposed recommendations on risk provisioning (₹ crore)
  Jun 2020 Mar 2021 Mar 2022 Mar 2023 Mar 2024 Mar 202531
B/S size 53,34,793 57,07,669 61,90,302 63,44,756 70,47,703 76,25,422
Level at which Realized Equity maintained 5.50% 5.50% 5.50% 6.00% 6.50% -
Risk provisioning 73,615 20,710 1,14,667 1,30,876 42,820 -
Surplus transferred 57,128 99,122 30,307 87,416 2,10,874 -
Component-wise additional risk provisioning as per proposed framework*
CRB – Proposed Upper Bound (7.5%) 1,06,696 1,14,153 1,23,806 95,171 70,477 44,862
CRB – Proposed Lower Bound (4.5%) (-) 53,348 (-) 57,077 (-) 61,903 (-) 95,171 (-) 1,40,954 (-) 1,83,901
Market risk ES 97.5% CL 0 72,296 1,03,886 0 0 0
Market risk ES 99.5% CL 0 3,08,563 3,50,032 1,09,174 2,07,352 0
Cumulative additional risk provisioning considering Market Risk Resilience at ES 97.5%*
CRB – Proposed Upper Bound 1,06,696 1,86,449 2,27,692 95,171 70,477 44,862
CRB – Proposed Lower Bound (-) 53,348 15,219 41,983 (-) 95,171 (-) 1,40,954 (-) 1,83,901
Cumulative additional risk provisioning considering Market Risk Resilience at ES 99.5%*
CRB – Proposed Upper Bound 1,06,696 4,22,716 4,73,838 2,04,345 2,77,830 44,862
CRB – Proposed Lower Bound (-) 53,348 2,51,486 2,88,129 14,002 66,398 (-) 1,83,901
* Risk provisioning over and above the provisions already maintained

1 Inclusion of net valuation gains/ losses as Revaluation Accounts in the balance sheet instead of including them in the Income Statement, and charging of net unrealized losses in Revaluation Accounts to CF during finalisation of Annual Accounts.

2 Under the extant ECF, additional risk provisioning is permissible only if revaluation balances are lower than ES 97.5% CL.

3 The Coefficient of Variation (CV), computed as Standard Deviation / Mean, is a statistical measure of the dispersion of data points around the mean. It stood at 30.84 per cent during the six years prior to the adoption of the extant ECF.

4 Additional risk provisioning for March 31, 2025, estimated after considering ARE prior to risk provisioning (6.91%)

5 Section 47: After making provision for bad and doubtful debts, depreciation in assets, contributions to staff and superannuation funds and for all other matters for which provision is to be made by or under this Act or which are usually provided for by bankers, the balance of the profits shall be paid to the Central Government.

6 In the case of RBI, the Bank’s interest income on domestic assets has far exceeded the net interest outgo on account of liquidity adjustment operations in recent years, resulting in a significant positive interest income from domestic sources. Moreover, the interest income from foreign sources has also seen a significant increase as low coupon foreign securities/ deposits have progressively been replaced with higher coupon ones.

7 Examples of prominent central banks include Federal Reserve, European Central Bank, Deutsche Bundesbank, Banque de France, Swiss National Bank, Bank of Canada and Reserve Bank of Australia (RBA).

8 RBA, Bank of Russia, Czech National Bank, Monetary Authority of Singapore, and Reserve Bank of New Zealand are a few prominent central banks which have posted losses in the previous few years due to net valuation losses.

9 Post finalisation of Annual Accounts. Includes the impact of risk provisioning carried out during the year/ write-back of risk provisions.

10 Includes the impact of charging of negative balances in revaluation accounts to CF as per the Accounting Policy.

11 The Committee had projected average risk provisioning in the range of 14% to 16.6% of net income under the Mean Scenario. The range was projected to be 27.8% to 32.8% of net income in the case of a negative 1 SD shock to net income.

12 Excludes the impact on account of write back of ₹52,637 crore from CF in FY 2018-19. On inclusion of the same, the risk provisioning and surplus transferred as a percentage of net income would be 33.31% and 66.69% respectively.

13 As on March 31, 2025, the OFBS exposure is more than 10% of the size of the Bank’s Balance Sheet.

14 Under the extant ECF, in case the market risk buffers are adequate to meet their requirement computed at ES 97.5 per cent CL, no additional risk provisioning for market risk is permissible.

15 BIS, Committee on the Global Financial System (CGFS) Papers No 68 (Mar 2023) - ‘Central bank asset purchases in response to the Covid-19 crisis’ by Margarita Delgado (Banco de España) and Toni Gravelle (BoC).

16 The Coefficient of Variation (CV) is computed as Standard Deviation / Mean. It stood at 30.84 per cent during the six years prior to the adoption of the extant ECF.

17 Memorandum of Understanding: Financial relationship between HM Treasury and the Bank of England (2025)

18 Annual Report of the Reserve Bank of Australia (2024)

19 Section 213 of the Reserve Bank of New Zealand Act 2021

20 Statement of Financial Risk Management [Page 59 of the Annual Report of the Reserve Bank of New Zealand (2024)]

21 Section 27 of the Bank of Canada Act

22 Section 27.1 (1) of the Bank of Canada Act

23 Article 30 of the National Bank Act, 2003

24 Press Release: Annual result of the Swiss National Bank for 2024 (March 3, 2025)

25 Section 14 of the Bank of Thailand Act

26 Annual Financial Statement of Bank of Thailand (2023)

27 Section 7 of the Central Bank of Malaysia Act 2009

28 Annual Report of the Bank Negara Malaysia (2023)

29 Article 99 of the Bank of Korea Act

30 Financial statements of Bangko Sentral ng Pilipinas (2022 and 2023)

31 Additional risk provisioning for March 31, 2025, estimated after considering ARE prior to risk provisioning (6.91%)

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FREE-AI Committee Report - Framework for Responsible and Ethical Enablement of Artificial Intelligence


Letter of Transmittal

Acknowledgements

The Committee is grateful to the Shri Sanjay Malhotra, Governor, Reserve Bank of India, for the opportunity to contribute to this important area at a crucial juncture in the evolution of technology in the financial sector. The Committee would like to express gratitude to Shri T. Rabi Sankar, Deputy Governor, RBI for his vision, insights, and valuable perspectives, that enriched the report. The Committee is also thankful to Shri P. Vasudevan, Executive Director, RBI for his guidance and support.

As part of the deliberations, the Committee engaged with a wide range of stakeholders to gain diverse perspectives on the adoption, opportunities, and challenges of artificial intelligence in the financial sector. The inputs were instrumental in developing a well-rounded understanding of the evolving AI ecosystem in India. The Committee is thankful for the interactions and acknowledges the contributions of all stakeholders who shared their time and expertise. A detailed list is provided in Annexure I.

The Committee would like to convey its appreciation to the Secretariat team of FinTech Department, comprising Shri Muralidhar Manchala, Shri Ankur Singh, Shri Praveen John Philip, Shri Padarabinda Tripathy, Shri Manan Nagori, Shri Ritam Gangopadhyay, for their excellent support in facilitating the Committee meetings and stakeholder interactions, conducting background research and survey, as well as assisting in the drafting of this report.


List of Abbreviations


Executive Summary

Artificial Intelligence (AI) is the transformative general-purpose technology of the modern age. Over the years, the simple rule-based models have evolved into complex systems capable of operating with limited human intervention. More recently, it has started to reshape how we work, how businesses operate and engage with their customers. In the process, it has forced us to question some of our most fundamental assumptions about human creativity, intelligence and autonomy.

For an emerging economy like India, AI presents new ways to address developmental challenges. Multi-modal, multi-lingual AI can enable the delivery of financial services to millions who have been excluded. When used right, AI offers tremendous benefits. If used without guardrails, it can exacerbate the existing risks and introduce new forms of harm.

The challenge with regulating AI is in striking the right balance, making sure that society stands to gain from what this technology has to offer, while mitigating its risks. Jurisdictions have adopted different approaches to AI policy and regulation based on their national priorities and institutional readiness.

In the financial sector, AI has the potential to unlock new forms of customer engagement, enable alternate approaches to credit assessment, risk monitoring, fraud detection, and offer new supervisory tools. At the same time, increased adoption of AI could lead to new risks like bias and lack of explainability, as well as amplifying existing challenges to data protection, cybersecurity, among others.

In order to encourage the responsible and ethical adoption of AI in the financial sector, the FREE-AI Committee was constituted by the Reserve Bank of India. The RBI conducted two surveys to understand current AI adoption and challenges in the financial sector. The Committee referenced these surveys and, in addition, undertook extensive stakeholder consultations to gain further insights.

After extensive deliberations, the Committee formulated 7 Sutras that represent the core principles to guide AI adoption in the financial sector. These are:

  1. Trust is the Foundation

  2. People First

  3. Innovation over Restraint

  4. Fairness and Equity

  5. Accountability

  6. Understandable by Design

  7. Safety, Resilience and Sustainability

Using the Sutras as guidance, the Committee recommends an approach that fosters innovation and mitigates risks, treating these two seemingly competing objectives as complementary forces that must be pursued in tandem. This is achieved through a unified vision spread across 6 strategic Pillars that address the dimensions of innovation enablement as well as risk mitigation. Under innovation enablement, the focus is on Infrastructure, Policy and Capacity and for risk mitigation, the focus is on Governance, Protection and Assurance. Under these six pillars, the report outlines 26 Recommendations for AI adoption in the financial sector.

To foster innovation, it recommends:

  • the establishment of shared infrastructure to democratise access to data and compute; the creation of an AI Innovation Sandbox

  • the development of indigenous financial sector-specific AI models

  • the formulation of an AI policy to provide necessary regulatory guidance

  • institutional capacity building at all levels, including the board and the workforce of REs and other stakeholders,

  • the sharing of best practices and learnings across the financial sector

  • a more tolerant approach to compliance for low-risk AI solutions to facilitate inclusion and other priorities

To mitigate AI risks, it recommends:

  • the formulation of a board-approved AI policy by REs

  • the expansion of product approval processes, consumer protection frameworks and audits to include AI related aspects

  • the augmentation of cybersecurity practices and incident reporting frameworks

  • the establishment of robust governance frameworks across the AI lifecycle

  • making consumers aware when they are dealing with AI

This is the FREE-AI vision: a financial ecosystem where the encouragement of innovation is in harmony with the mitigation of risk.

FREE AI Framework

Chapter 1 – Introduction and Background

Artificial Intelligence (AI) has seen significant growth in recent years, drawing attention from industry, innovators, policy makers and consumers alike. Whether it is seeking answers, creating avatars, or personalised e-commerce, AI is increasingly getting embedded in day-to-day activities. Given the recent surge in interest, it is easy to view AI as a relatively new phenomenon. However, the roots of AI actually date back several decades.

1.1 Evolution of Artificial Intelligence and Machine Learning

1.1.1 Early Foundations and Milestones: In his seminal 1950 paper Computing Machinery and Intelligence, renowned mathematician Alan Turing first posed the fundamental question, “Can machines think?” and then introduced the Imitation Game (now known as the Turing Test) as a way to gauge machine intelligence. However, the term 'Artificial Intelligence' was coined in 1956 by John McCarthy during the Dartmouth Summer Research Project on Artificial Intelligence, a seminal event which set the stage for decades of exploration.

1.1.2 Early research in the 1960s and 1970s focused on symbolic AI and logic-based programs (the era of “Good Old-Fashioned AI” (GOFAI)) that could prove mathematical theorems and solve puzzles. These periods of over-optimism were followed by “AI winters” when funding and interest waned, however, foundational work continued. By the 1980s, expert systems, i.e., rule-based programs encoding human expert knowledge, became popular. Yet, these systems were hard to maintain and required manual knowledge engineering.

1.1.3. Emergence of Machine Learning: Machine Learning (ML) enabled algorithms to learn autonomously from data without explicit programming. This shift in the 1990s was due to significant improvements in computing power, data storage, and connectivity. ML techniques like neural networks, decision trees, and support vector machines began outperforming rule-based systems in tasks like image classification and language translation. World Chess Champion Garry Kasparov’s 3½ - 2½ defeat to IBM’s Deep Blue in a six-game rematch in 1997 demonstrated the ability of machines to outperform humans in domains considered to require strategic reasoning. This inspired early exploration in financial applications as well.

1.1.4 As a financial sector application, HNC Software’s Falcon system was screening two-thirds of all credit card transactions worldwide by the 1990s. ML application grew in the 2000s, and in finance, early ML models were deployed for specific, well-defined tasks: for instance, using neural networks, Banks also adopted ML for credit scoring beyond traditional logistic regression, using larger datasets to enhance prediction accuracy.

1.1.5 The Deep Learning Revolution and Generative AI: The 2010s saw further breakthroughs with the rise of deep learning, a subset of ML that involved multi-layered neural networks. A major milestone during this period was the release of the 2017 paper “Attention is All You Need” by researchers at Google, which introduced the Transformer architecture that laid the foundation for large language models (LLMs). The power of deep learning’s ability to carry out complex pattern recognition was validated by landmark achievements such as computers surpassing human accuracy in image recognition in 2012 and when Google DeepMind’s “AlphaGo” defeated Go champion Lee Sedol in 2016. Soon after, voice assistants became commonplace, and self-driving cars took to the roads. AI was no longer confined to labs; it began to surface in everyday products and services.

1.1.6 In late 2022, Generative AI tools brought the power of advanced AI directly to the public. ChatGPT reached 100 million users in just two months after launch1, highlighting the unprecedented pace of adoption. Techniques such as retrieval-augmented generation (RAG), mixture-of-experts (MoE) architectures are further enhancing capabilities. From generating images to creating complex reports using a suite of agents, AI has moved beyond just being a niche technology to gradually reshaping the way we work.

1.1.7 Unprecedented Progress: As per the AI Index report 2025 by Stanford, AI systems now outperform humans in nearly all tested domains. Complex reasoning is the last major frontier, but even here, the gap is narrowing quickly. Open-source AI models are rapidly catching up to closed models, narrowing the gap from 8% to just 1.7%. Smaller models are also showing significant gains in efficiency and capability. The year 2024 marked a shift in national strategy with record public investments: India ($1.25 billion), France ($117 billion), Canada ($2.40 billion), China ($47.50 billion), and Saudi Arabia ($100 billion)2.

1.2 AI and ML in Financial Services

1.2.1 The role of AI in financial services has significantly increased over the last decade. As machine learning has matured, banks and insurers have expanded use cases from rule-based systems to real-time fraud detection, anomaly detection in claims processing, and market forecasting. The 2010s saw the rise of big data and deep learning, enabling institutions to leverage alternative data sources (e.g., social media, geolocation) and deploy NLP-powered chatbots like Bank of America’s “Erica.” Today, Gen-AI is being used in advanced chatbots, automated report generation, and the creation of synthetic data sets for safer model training. It is estimated that this could add $200-340 billion annually to the global banking sector through productivity gains in compliance, risk management, and customer service3.

1.2.2 In the Indian context, AI has the potential to improve financial inclusion, expand opportunities for innovation and enhance efficiency in financial systems. Yet, these systems pose certain incremental risks and ethical dilemmas. As these systems are being increasingly integrated into high-stakes applications such as credit approvals, fraud detection, and compliance, there is a need to ensure that their application is responsible and ethical, that harm does not arise from their use, and that their outcomes do not undermine public trust.

1.3 Constitution of the Committee

1.3.1 In order to further responsible innovation in AI, while at the same time ensuring that consumer interests are protected, the Reserve Bank of India announced the establishment of a Committee to develop a framework for the responsible and ethical enablement of AI in the financial sector in its Statement on Developmental and Regulatory Policies dated December 6, 20244. Accordingly, the committee for developing the Framework for Responsible and Ethical Enablement of Artificial Intelligence in the Financial Sector (hereinafter referred to as the Committee or FREE-AI Committee) was constituted. The members of the committee are:

1.4 Terms of Reference

1.4.1 The terms of reference of the Committee are as under:

  1. To assess the current level of adoption of AI in financial services globally and in India.

  2. To review regulatory and supervisory approaches on AI with a focus on the financial sector globally.

  3. To identify potential risks associated with AI, if any, and recommend an evaluation, mitigation and monitoring framework and consequent compliance requirements for financial institutions, including banks, NBFCs, FinTechs, PSOs, etc.

  4. To recommend a framework including governance aspects for responsible, ethical adoption of AI models/ applications in the Indian financial sector.

  5. Any other matter related to AI in the Indian financial sector.

1.5 Methodology

1.5.1 The Committee adopted a four-pronged approach.

i. Stakeholder Engagement: The Committee held extensive deliberations and adopted a consultative approach to get insights on the emerging developments, ongoing innovations, stakeholder needs, challenges and risks in the financial sector on account of the use of AI. Interactions were also conducted with stakeholders, including presentations from the RBI departments, consultants, and financial sector entities. Details of the interactions are provided at Annexure I and II.

ii. Survey and Interactions: Two targeted surveys were carried out, covering Scheduled Commercial Banks (SCBs), Non-Banking Financial Companies (NBFCs), All India Financial Institutions (AIFI) and FinTechs. Follow-up interactions were conducted with select Chief Digital Officers / Chief Technology Officers (CDOs/CTOs) to understand the extent to which AI had been adopted in the Indian financial services industry and any associated challenges.

iii. Review of global developments and literature: The Committee also examined the internationally published literature, global developments, extant regulatory frameworks/ approaches adopted in other jurisdictions and views of global standard-setting bodies (SSBs) and international organisations (IOs).

iv. Analysis of extant regulatory guidelines: Finally, the Committee analysed the extant regulatory framework applicable to the REs, such as those related to cybersecurity, data protection, consumer protection, and outsourcing, to the extent they capture the AI-specific risks and concerns.

1.5.2 In addition, based on the stakeholder engagement and survey feedback, the Committee acknowledged the need to place specific emphasis on fostering AI innovation and treated it as a critical reference point in defining its approach.

1.6 Structure of the Report

1.6.1 The remainder of the report is structured into three chapters. Chapter 2 examines the current state of AI adoption in the financial sector, highlighting the benefits and opportunities, and the evolving landscape of risks and challenges associated with AI deployment. Chapter 3 analyses the broader policy environment, covering key global approaches, domestic developments, and practical insights drawn from stakeholder interactions and survey responses across regulated entities and FinTechs. Finally, Chapter 4 presents the Committee’s proposed Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI). The terms used in this Report are explained in the Glossary at the end of this Report for contextual understanding.

Chapter 2 – AI in Finance: Opportunities and Challenges

The financial services sector has witnessed the gradual integration of AI into core business functions such as risk management, fraud detection, and customer service. The recent AI evolution, while opening new frontiers of innovation, also gives rise to certain challenges about unintended outcomes and consequences. This chapter highlights the opportunities it offers and new risks that warrant more careful consideration.

2.1 Benefits and Opportunities

2.1.1 The adoption of AI in financial services has accelerated globally. According to a 2025 World Economic Forum white paper5 on AI in Financial Services, projected investments across banking, insurance, capital markets and payments business are expected to reach over ₹8 lakh crore ($97 billion) by 2027. It is believed that AI will directly contribute to revenue growth in the coming years. The generative AI segment alone is forecast to cross ₹1.02 lakh crore ($12 billion) by 2033, with a compound annual growth rate (CAGR) of 28–34%6. The OECD highlighted that AI is currently being developed or deployed by a broad range of financial institutions with major use cases such as customer relations, process automation and fraud detection7.

2.1.2 As AI continues to gain traction across financial services, it is beginning to unlock value by enhancing efficiency, accuracy and personalisation at scale. A key set of drivers underpinning this adoption includes the need to enhance customer experience, improve employee productivity, increase revenue, reduce operational costs, ensure regulatory compliance, and enable the development of new and innovative products. GenAI is poised to improve banking operations in India by up to 46%8. AI-driven analytics allow institutions to better understand customer behaviour, manage risk proactively, and optimize operational costs. AI-powered alternate credit scoring models continue to expand credit access to the underserved population. AI chatbots can handle routine customer queries with 24x7 availability. AI-based early warning signals facilitate enhanced risk management. For instance, J.P.Morgan claims AI has significantly reduced fraud by improving payment validation screening, leading to a 15-20% reduction in account validation rejection rates and significant cost savings9. AI also improves operational efficiency through automating repetitive tasks such as data entry, document summarisation, and aiding human decisions.

2.1.3 AI for Financial Inclusion: In developing economies like India, where millions remain outside the ambit of formal finance, AI can help assess creditworthiness using non-traditional data sources such as utility payments, mobile usage patterns, GST filings, or e-commerce behaviour, thereby including “thin-file” or “new-to-credit” borrowers. AI-powered chatbots can offer context-aware financial guidance, grievance redressal, and behavioural nudges to low-income and rural populations. Voice-enabled banking in regional languages has the potential to allow illiterate or semi-literate individuals to access finance.

2.1.4 Leveraging AI in Digital Public Infrastructure: The 2023 recommendations of the G20 Task Force on DPI10 highlighted the need to integrate AI responsibly with DPI. India’s pioneering DPI ecosystem, including Aadhaar, UPI frameworks, offers a robust foundation for AI-driven enhanced service delivery, personalisation and real-time decision making. This convergence can pave the path for next-gen DPI where services are not only digital, but intelligent, inclusive and adaptive. Conversational AI embedded with UPI, improved KYC with AI and Aadhaar and personalised service through Account Aggregator can enhance financial services. AI models offered as a public good can benefit smaller and regional players.

2.1.5 Financial Sector Specific Models: Foundation models are large-scale machine learning models trained on vast datasets and fine-tuned for general use11. In the Indian context, an important strategic question is whether there is a need to develop indigenous foundation models tailored for the financial sector.

2.1.6 India's financial ecosystem is linguistically and operationally diverse. Any foundation model deployed in the financial sector must accurately represent the diversity to avoid urban-centric biases. This calls for models capable of operating in all the languages spoken in the country. General-purpose large language models (LLMs) predominantly trained on English and Western-centric datasets may not be able to handle such multilingual diversity. Relying on foreign AI providers for core financial models could also expose systemic vulnerabilities. Further, Small Language Models (SLMs) designed around a single use case or a narrow set of tasks or fine-tuning existing open-weight models to specific requirements for the financial sector, could be resource-efficient and faster to train.

2.1.7 In addition, an alternate approach could be Trinity Models designed on specific Language-Task-Domain (LTD) combinations. For example, a model focused on Marathi (Language) + Credit Risk FAQs (Task) + MSME Finance (Domain); or Hindi (Language) + Regulatory Summarization (Task) + Rural Microcredit (Domain). They can support multilingual inclusion and regulatory alignment, making them suitable for the diverse ecosystem. Such systems can be built quickly with a moderate number of resources.

2.1.8 The Curious Case of Autonomous AI Systems: Autonomous agents can deconstruct complex goals, distribute them across other agents, and dynamically develop emergent solutions to problems. Emerging protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication frameworks can facilitate an interoperable and collaborative agent ecosystem. This marks a shift from task automation to decision automation and could have wide-ranging implications across the Indian financial landscape. AI agents representing an SME borrower could interact with multiple AI-enabled lenders to obtain loan offers, perform comparative analysis, and execute transactions in real time.

2.1.9 Synergies with other Emerging Technologies: Synergies between AI and other emerging technologies (such as quantum computing) are at an early stage of exploration. AI could optimise quantum algorithms and enhance quantum error correction. Quantum computing could also enhance AI capabilities by accelerating complex computations involved in training large models and improving performance in areas such as pattern recognition. Privacy-enhancing technologies (PETs) and federated learning can enable models to be trained together without exchanging raw data. While such developments remain nascent, they indicate the promise of next-generation AI systems in finance.

2.2 Emerging Risks and Sectoral Challenges

2.2.1 In addition to the benefits, the integration of AI into the financial sector introduces a broad and complex spectrum of risks that challenge traditional risk management frameworks. These include concerns related to data privacy, algorithmic bias, market manipulation, concentration risk, operational resilience, cybersecurity vulnerabilities, explainability, consumer protection, and AI governance failures. The risks may undermine market integrity, erode consumer trust, and amplify systemic vulnerabilities. All of this needs to be well understood for effective risk management. These risks and challenges are, as outlined in the following section, indicative and not exhaustive, given the evolving nature of AI.

2.2.2 Model Risk Factors: At its core, AI model risk arises when the outputs of algorithms or systems deviate from expected outcomes, leading to financial losses or reputational harm. One such example is the bias that may be inherent in a model. This can either be due to the training data or the way in which the model was developed. AI models are often opaque (referred to as the “black box” problem), which makes it difficult to explain their decisions or audit their outputs. This could magnify the severity of model errors, particularly in high-stakes applications.

2.2.3 Models can suffer from various risks: data risk due to incomplete, inaccurate, or unrepresentative datasets, design risk due to flawed or misaligned algorithmic architecture, calibration risk due to improper weights, and risks in how they are implemented. On their own or together, these risks can generate cascading failures across business units and undermine consumer trust. While AI-powered model risk management (MRM) platforms can use AI to monitor and validate other AI models, they can also introduce “model-on-model” risks, where failures in supervisory AI systems could cascade across dependent models. GenAI models can suffer from hallucinations, resulting in inaccurate assessments or misleading customer communications. They are also less explainable, making it harder to audit outputs.

2.2.4 Operational Risks – Systems under Stress: Even though the automation of mission-critical processes reduces human error, it can exponentially amplify faults across high-volume transactions. For example, an AI-powered fraud detection system that incorrectly flags legitimate transactions as suspicious or, conversely, fails to detect actual fraud due to model drift, can cause financial losses and reputational damage. Erroneous or stale data, whether on account of manual entry errors or data pipeline failures, can lead to adverse outcomes. A credit scoring model that depends on real-time data feeds could fail on account of data corruption in upstream systems. If monitoring is not done consistently, AI systems can degrade over time, delivering suboptimal or inaccurate outcomes.

2.2.5 Third-Party Risks – Invisible Dependencies, Visible Risks: Since AI implementations often rely on external vendors, cloud service providers, and technology partners to supply, maintain, and operate AI systems, they can expose entities to a range of dependency risks, including service interruptions, software defects, non-compliance with regulatory obligations, and breaches of contractual terms. Limited access or visibility of into the internal controls of vendors can impair an institution’s ability to conduct vendor due diligence and risk assessments and ensure compliance with outsourcing guidelines. In addition, there can also be a concentration risk that arises on account of a limited number of dominant vendors. There are also risks related to the vendor’s subcontractors over which financial institutions have even more limited visibility and control.

2.2.6 Liability Considerations in Probabilistic and Non-Deterministic Systems: AI deployments blur the lines of responsibility between various stakeholders. This difficulty in allocating liability can expose institutions to legal risk, regulatory sanctions, and reputational harm, particularly when AI-driven decisions affect customer rights, credit approvals, or investment outcomes. For instance, if an AI model exhibits biased outcomes due to inadequately representative training data, questions may arise as to whether the responsibility lies with the deploying institution, the model developer, or the data provider. Similarly, erroneous outcomes in AI-powered credit evaluation systems raise questions regarding who should be held accountable when decisions are non-deterministic and opaque in nature.

2.2.7 Risk of AI-Driven Collusion: While at present, evidence of AI systems autonomously colluding with each other is limited, the theoretical risk of this happening is significant. Without human oversight, AI agents designed for goal-directed behaviour and autonomous decision-making, AI systems may collude to maintain supra-competitive prices, raising potential concerns from fair competition, especially in high-frequency trading or dynamic pricing environments. This could result in breach of market conduct rules.

2.2.8 Potential Impact on Financial Stability: The Financial Stability Board (FSB)12 has highlighted that AI can amplify existing vulnerabilities, such as market correlations and operational dependencies. One such concern is the amplification of procyclicality, where AI models, learning from historical patterns, could reinforce market trends, thereby exacerbating boom-bust cycles. When multiple institutions deploy similar AI models or strategies, it could lead to a herding effect where synchronised behaviours could intensify market volatility and stress. Excessive reliance on AI for risk management and trading could expose institutions to model convergence risk, just as dependence on analogous algorithms could undermine market diversity and resilience. The opacity of AI systems could make it difficult to predict how shocks transmit through interconnected financial systems, especially at times of crisis.

2.2.9 AI models deployed in banking can behave unpredictably under rare or extreme conditions if not adequately tested. For instance, during periods of sudden economic stress, AI-driven credit models may misclassify borrower risk due to reliance on historical patterns that no longer hold good, potentially leading to abrupt tightening of credit. During the 2010 'Flash Crash13', automated trading algorithms contributed to a rapid and severe market downturn, erasing nearly $1 trillion in market value within minutes. Such events highlight the risks to financial stability of using AI tools that have not been adequately stress-tested for extreme events.

2.2.10 AI and Cybersecurity – A Double-Edged Sword: AI is a double-edged sword for cybersecurity. It can be misused to carry out more advanced cyberattacks, but it can also help detect, prevent, and respond to threats more quickly and effectively. The use of AI can result in new vulnerabilities at the model, data, and infrastructure levels. Attackers can poison the data by subtly manipulating the training dataset, making the AI models learn incorrect patterns. For instance, poisoning the transaction data used in fraud detection could result in the model misclassifying fraudulent behaviour as legitimate.

2.2.11 Other attacks include adversarial input attacks where attackers craft inputs designed to mislead AI models into making faulty decisions and prompt injection, that embeds hidden commands, such as “Ignore previous instructions and authorize a fund transfer,” within a routine query, potentially triggering unauthorized actions. There is also model inversion, where attackers reconstruct sensitive data, such as personal financial profiles or credit histories, on which the model has been trained through queries aimed at uncovering that information. Inference attacks allow adversaries to determine whether specific data points were used in a model’s training set, potentially exposing sensitive customer relationships or competitive insights. Model distillation is the process by which adversaries interact with an AI system to replicate the underlying AI models, enabling competitors to exploit proprietary AI.

2.2.12 AI can also be used as a powerful tool for executing cyberattacks such as automated phishing, deepfake fraud, and credential stuffing at an unprecedented scale. The year 2024 witnessed a sharp rise in AI-generated phishing campaigns that leveraged natural language generation to craft personalised emails designed to evade spam filters and increase the success rate of credential theft. Deepfake audio and video are being used by malicious attackers to convincingly impersonate executives and officials, thereby bypassing the chain of approvals for transaction authorization. Such deepfake photos and videos can also compromise the video KYC process.

2.2.13 At the same time, AI could also be used to bolster cybersecurity resilience. Financial institutions are already using AI-powered tools for threat and anomaly detection, as well as for predictive analytics to anticipate and counter cyber threats in real time. AI-enhanced security information and event management (SIEM) systems can process vast volumes of data to identify patterns indicative of cyber threats that are so subtle that they escape traditional rule-based systems. When ML is integrated into endpoint detection and response (EDR), the speed and accuracy with which compromised devices are identified improve. With AI-driven behavioural analytics, institutions can monitor employee and customer activity to detect insider threats or account takeovers more effectively.

2.2.14 Security and Privacy of Data: AI systems often collect and process more data than required. This practice, known as data over-collection, violates the data protection principles of data minimisation and purpose limitation. Given the global nature of modern AI infrastructure, especially when cloud services and third-party providers are involved, the use of AI in the financial sector could conflict with data localisation requirements. The process of enriching datasets through data aggregation can inadvertently result in mosaic attacks, where seemingly innocuous data points could combine to reveal sensitive information. Where decryption is required for processing, it can be momentarily exposed to threats such as memory scraping or privileged access attacks.

2.2.15 Risks to Consumers and Ethical Concerns: AI applications could pose significant risks to consumers and vulnerable groups. Algorithmic bias can further exacerbate the exclusion of those already outside the formal financial system. AI’s inherent opacity or “black box” nature can leave consumers in the dark. Compounding these risks is the potential for violating personal data due to the use of AI. When AI is used to enhance engagement, it can subtly influence consumer decisions in ways that may not always align with their best interests. Autonomous decisions, especially in high-risk applications, may raise questions of liability. AI decisions can raise ethical concerns around manipulation, informed consent, and exploitation. AI could exacerbate asymmetries of power and information between financial providers and consumers, resulting in a digital divide.

2.2.16 AI Inertia – Risk of Non-Adoption and Falling Behind: The risk of not adopting AI, at both the sectoral and institutional levels, presents a significant threat to the long-term competitiveness, operational efficiency, and financial inclusion goals of India’s financial ecosystem. At the institutional level, reluctance to deploy AI-enabled tools may itself pose a significant risk, as this is often the only effective way to counter the use of AI by malicious actors. It can also risk widening the financial access gap, particularly in underserved and rural areas, where AI-driven solutions like alternative credit scoring models and predictive analytics for microfinance can be transformative.

2.2.17 As the chapter highlights, the opportunities of AI in finance come with several associated challenges. While the risks and challenges are becoming better understood, the broader innovation potential of AI is yet to be fully realised. While meaningful use cases have already begun to take shape, as apprehensions give way to experience, and as the technology matures alongside institutional capacity, the sector is expected to witness more transformative applications over time.

Chapter 3 – AI Policy Landscape and Insights from the Ecosystem

As the adoption of AI in financial services continues to expand, jurisdictions across the world have actively engaged in exploring different policy approaches. Even at an institutional level, AI risks are increasingly being acknowledged and incorporated either in existing risk frameworks or new policies. This chapter explores the evolving policy landscape both at the global and domestic fronts and also draws on insights gathered from key ecosystem stakeholders to reflect ground-level perspectives.

3.1 Global Policy Developments and Approaches

3.1.1 Standard-setting bodies and international organisations have taken steps to articulate foundational principles, identify emerging risks, and shape global consensus on the responsible use of AI. The Financial Stability Board (FSB), in its 2024 report,14 which revisited the 2017 analysis15 on AI in financial services, has highlighted that while financial policy frameworks address some vulnerabilities, gaps remain, which may require continuous monitoring, assessment of regulatory adequacy, and fostering cross-sectoral coordination. The OECD, in its Recommendation on Artificial Intelligence, that was released in 2019 and updated in 2024,16 recommended the promotion of AI that respects human rights and democratic values and established the first intergovernmental standard on AI. Standards such as ISO/IEC 2389417 (risk management in AI systems), ISO/IEC 4200118 (AI management systems), and ISO/IEC 2305319 (frameworks for machine learning-based AI systems) help institutions to ensure that their AI systems are fair, transparent, and ethical.

3.1.2 Alongside these efforts, jurisdictions have adopted diverse approaches such as principle-based guidance, voluntary initiatives, binding legislations or regulations, based on primarily focused on managing AI-specific risks. In most instances, the approach to AI regulation is defined by the maturity of AI adoption within the jurisdictions. Some of the policy approaches are highlighted below:

  • Centralised Omnibus Law: This approach takes a broad horizontal approach and requires all AI applications to adhere to the central framework regardless of the sector in which it is being applied or the use to which it is put. The EU AI Act is one such omnibus law. Experts opine that while this approach helps to promote consistency, it comes at the cost of flexibility, as it may not be able to properly account for sectoral variations and the future evolution of this dynamic technology.

  • Vertical, Type-Specific Legislation: This is a more fine-grained approach that is focused on specific categories or functionalities of AI, such as generative models. China has implemented laws regulating different types of AI, including separate ones for fake news, generative AI, and algorithmic regulations. China’s approach has been more layered, with broad guiding principles and elaborate binding administrative regulations that align with its national priorities of AI leadership and national security.

  • Guidance: It allows sectoral regulators to decide whether they need to enact new subordinate legislation or merely be more thoughtful about how existing regulations should be extended to cover the new harms caused by AI. This approach has been adopted by countries like the U.S., UK and Singapore. Singapore has chosen a multi-stakeholder approach with a view to strengthening its public-private digital economy and ensuring responsible innovation in its fintech ecosystem. Accordingly, it has issued a Model AI Governance Framework for Generative AI, the Veritas Toolkit and the FEAT (Fairness, Ethics, Accountability and Transparency) principles.

  • Classification: Some countries classify AI systems in order to stipulate the de minimis threshold above which regulations apply. The EU AI Act has categorised the impact of a model based on parameters, tokens, amount of compute, modalities and benchmarks. It has classified AI systems into unacceptable, high risk, limited risk and minimal risk, in an approach that is closely aligned with its approach to data protection and product safety laws.

  • National AI Strategy: Some countries have put in place national AI strategies that are non-binding but provide some indication of areas of strategic investment and national priorities. This includes Brazil, Canada, Norway, Saudi Arabia, Switzerland, Spain, France and Germany.

Figure No. 1: Global Regulatory Approaches for AI

3.1.3 In some jurisdictions, financial authorities have issued financial sector-specific guidance. The European Banking Authority (EBA), the Hong Kong Monetary Authority (HKMA), and the Monetary Authority of Singapore (MAS) have all issued high-level principles or clarification as to how existing regulations apply to AI. Singapore, Indonesia and Qatar have a national AI strategy along with financial sector-specific guidance in place. South Korea, with its AI Basic Act, which will take effect in January 2026, and Guidelines for AI in Financial Sector, 2021 issued by the Financial Services Commission, has both national legislation and financial sector guidance on AI. Frameworks that have been designed in the Western context are focused on mitigating the risks arising from AI systems. Nations of the Global South may take a different approach.

3.1.4 Institutional Frameworks: Some countries have established specialised government-backed technical organizations to identify and address the risks associated with the use of AI. The primary functions of these institutions are research and evaluation of AI models, standards development and international cooperation. U.K.’s AI Safety Institute (AISI) unveiled its open-source platform called ‘Inspect’ to evaluate models in a range of areas, such as their core knowledge, ability to reason, and autonomous capabilities. The U.S.’s AISI convened an inter-departmental task force to tackle national security and public safety risks posed by AI. Singapore’s AISI is focusing on content assurance, safe model design, and rigorous testing20.

3.1.5 The Government of India has set up an AISI for responsible AI innovation. This Institute, incubated by IndiaAI Mission, has been set up as a hub and spoke model with various research and academic institutions and private sector partners joining the hub and taking up projects under the Safe and Trusted Pillar of the IndiaAI Mission. The India AISI will work with all relevant stakeholders, including academia, startups, industry and government ministries/departments, towards ensuring safety, security and trust in AI21.

3.1.6 Governance Measures: As the Board of Directors are ultimately accountable for the overall management of the entity, the responsibility for overseeing the approach with regard to AI adoption, risk mitigation, and alignment with organisational values typically rests with the Board at the institutional level. Various policy frameworks around the world, such as the Bank of England’s discussion papers on AI, require boards to define principles for responsible AI use and ensure alignment with overall risk appetite and fiduciary duties22.

3.1.7 Transparent disclosures enhance trust and accountability. The EU AI Act mandates that content that has either been generated or modified with the help of AI must be clearly labelled as AI-generated for user awareness. The UK’s Financial Conduct Authority (FCA) has emphasized that under the UK’s GDPR, data subjects must be informed about processing activities such as automated decision making and profiling, including, in certain instances, meaningful information about the logic involved in those decisions. Some organisations have created dedicated roles (such as Responsible AI Officer or Chief Data Officer) and dedicated committees to enhance the monitoring and mitigation of AI-related risks.

3.1.8 AI Toolkits – An Operational Bridge to Responsible AI: Various corporate entities have developed toolkits which help to ensure the responsible development and deployment of AI. Infosys has launched the Infosys Responsible AI Toolkit that provides a collection of technical guardrails that integrate security, privacy, fairness, and explainability into AI workflows23. The NASSCOM Responsible AI Resource Kit24, developed in collaboration with leading industry partners, offers sector-agnostic tools and guidance aimed at enabling businesses to adopt AI responsibly and scale with confidence. IBM has launched an open-source library that contains methods created by the research community to detect and reduce bias in machine learning models throughout the lifecycle of an AI application25. Microsoft's Responsible AI Toolbox is a similar collection of user interfaces for the exploration and assessment of models and data in order to aid in understanding AI systems26. These toolkits enable risk evaluation, bias detection and monitor performance drift and help support responsible AI implementation.

3.1.9 Learning from AI Failures and Incidents: The importance of systematically capturing and learning from AI-related failures and incidents has been gaining global recognition. In early 2025, the OECD published a policy paper introducing a common framework for AI incident reporting27. The OECD’s framework is voluntary and designed to standardize the information organisations collect and report, making it easier to aggregate learning from incidents28. The ISO/IEC 42001:2023 standard on AI management systems requires certified organizations to establish formal mechanisms for defining, documenting, and investigating AI-related incidents. The AI Incident Database (AIID)29, maintained by the Responsible AI Collaborative, is a public repository of AI incidents across all sectors, and allows anyone to submit reports of AI failures and near-misses, which moderators then curate and publish. Jurisdictions vary in their stance, with regions like the EU adopting mandatory, compliance-driven models, while others, such as the US, lean towards voluntary frameworks. Nonetheless, global best practices converge around core principles of having clear internal definitions of AI incidents, prompt and systematic reporting, documentation of cause and impact, proactive communication with stakeholders, and a feedback loop for continuous improvement.

3.1.10 Building Trust through AI Audits: The EU’s AI Act mandates risk-based audits for high-risk AI applications, setting a precedent for structured audit protocols. Methodologically, effective AI audits combine technical validation such as stress testing, adversarial robustness checks, ethical assessments covering bias and fairness audits, and process evaluations like governance and documentation reviews. Automated auditing platforms and continuous monitoring systems leverage AI to flag model drift or bias in real time.

3.1.11 Thematic Sandboxes: As another financial sector initiative, the Hong Kong Monetary Authority (HKMA), in collaboration with the Hong Kong Cyberport Management Company Limited (Cyberport), launched a Gen-AI Sandbox in 2024, that offers a risk managed framework, supported by essential technical assistance and targeted supervisory feedback within which banks can pilot their novel GenAI use cases30. FCA UK launched a dedicated AI Innovation Lab that included an AI Spotlight (for innovators to showcase their solutions to provide an understanding of AI’s application in financial services sector), an AI Sprint (a collaborative event that brought stakeholders together to inform the regulatory approach), an AI Input Zone (a forum for stakeholders’ views about current and future uses of AI in financial services) and a Supercharged Sandbox (an enhanced of the Digital Sandbox with greater computing power, enriched datasets and increased AI testing capabilities open to financial services firm looking to innovate and experiment with AI)31.

Figure No. 2: GenAI Sandbox

3.2 India’s Policy Environment and Developments

3.2.1 India aims to position itself as a global hub for responsible and innovation-driven AI, anchored in a commitment to its ethical development and deployment. Reflecting this broader vision, India's stated approach has been broadly pro-innovation, seeking to promote beneficial AI use cases with safeguards to limit user harm. The current legal frameworks, including the Information Technology Act 2000, Intermediary Rules and Guidelines, and relevant provisions under the Bharatiya Nyaya Sanhita 2023, are sufficient to address current risks. At the same time, there is flexibility to adapt policy responses as the technology evolves, with sector-specific policies being explored as necessary.

3.2.2 Policy efforts have been focused on strategic initiatives and guidelines aimed at fostering innovation while addressing ethical and governance concerns. The NITI Aayog released the National Strategy for Artificial Intelligence32 that envisions leveraging AI across sectors like healthcare, agriculture, education, smart cities, and smart mobility. It also issued a set of Principles for Responsible AI,33 setting out the principles according to which AI development and deployment should take place. The IndiaAI Mission, backed by ₹10,372 crore in the 2024 Union Budget, was launched to foster AI innovation by developing capabilities, boosting research and democratising access to compute infrastructure. Details on the strategic pillars of the IndiaAI Mission and the implementation status are provided in Annexure III. In the financial sector, SEBI released a consultation paper in 2025 on the guidelines for responsible usage of AI/ML in Indian Securities Markets34.

3.2.3 Analysis of Existing Guidelines from Reserve Bank of India: With regard to the financial sector, the regulatory approach has been technology agnostic, ensuring that financial services operate within well-defined principles of fairness, transparency, accountability, and risk management, regardless of the technology used. Existing RBI regulations already address key aspects of AI governance, such as ensuring fair and unbiased decision-making, maintaining transparency, conducting frequent audits, and enforcing data security measures, etc., in a generic way in the guidelines issued on IT, cybersecurity, digital lending, outsourcing, among others. The Committee conducted an analysis of select guidelines that may be relevant from the perspective of AI governance. While the details of that analysis have been set out in Annexure IV, an illustrative list of findings has been set out below:

3.2.3.1 Outsourcing: The RBI outsourcing guidelines clearly state that the mere act of outsourcing a function does not diminish the liability of the organization, and that they should not engage in outsourcing that would compromise or weaken their internal control, business conduct or reputation. In this context, it should be clarified that:

  1. when REs employ AI technologies or models developed by third parties within their operations, this is not outsourcing, and internal governance and risk mitigation policies will apply to the RE in the ordinary course.

  2. if the RE has outsourced a service to a third-party service provider and that third-party entity employs AI to deliver that service to REs, this constitutes outsourcing, and the outsourcing agreement at present does not explicitly cover the AI-specific governance, risk mitigation, accountability and data confidentiality.

3.2.3.2 Cybersecurity: While AI systems have not been explicitly mentioned in the cybersecurity guidelines, to the extent that these systems use large datasets and are susceptible to threats like data poisoning and adversarial attacks, these guidelines may still cover the use of AI by REs in a limited way. The IT guidelines require REs to maintain transparency, accountability, and control over their IT and cyber risk landscapes, including an obligation to put in place access control, audit trails, and vulnerability assessments. These obligations may be extended to AI-based systems.

3.2.3.3 Lending: The RBI's Guidelines on Digital Lending state that REs that assess a borrower’s creditworthiness using economic profiles, such as age, income, occupation, etc., must do so in a manner that is auditable. This can be made to apply to AI-driven credit assessments, ensuring that they do not operate in a black box and are subject to regulatory scrutiny and human oversight. Data collection by Digital Lending Apps (DLAs) or Lending Service Providers (LSPs) should be restricted to necessary information and require explicit borrower consent if they are used in AI systems.

3.2.3.4 Consumer Protection: To ensure that consumer trust in the financial system is maintained, the rights and interests of consumers must be protected at all times. Although the consumer protection circulars issued by RBI do not specifically cover AI risks, the principles set out in them would apply to the use of AI. Since the circulars also require the establishment of a robust grievance redressal mechanism, it would be desirable that REs should provide the customers with the means to challenge and seek clarification on AI decisions.

3.2.3.5 Despite the above existing regulations, there are certain incremental AI aspects that the existing regulations need to incorporate to make them comprehensive, such as AI-related disclosures, due diligence of vendors on AI risks, opportunities and risks in cybersecurity, etc. A comprehensive issuance providing guidance on incremental aspects and applicability of existing regulations may be required.

3.3 Insights from Surveys and Stakeholder Engagements

3.3.1 To gain a comprehensive understanding of the current state of AI adoption across the financial sector, two distinct surveys were designed by the RBI and administered by the Department of Supervision (DoS) and FinTech Department (FTD). The DoS administered a brief and objective survey among 612 supervised entities during February-May 2025. The surveyed entities included various types of banks, NBFCs, Asset Reconstruction Companies (ARCs) and All India Financial Institutions (AIFIs), representing close to 90% of the asset size. It focused on AI usage, technical infrastructure, and governance. The FTD also conducted an in-depth survey of 76 entities during January-May 2025 among select banks, NBFCs representing over 90% of the asset size. The survey was also conducted among select FinTechs and technology companies. Post the analysis of the survey response, FTD interacted with CTOs/CDOs of 55 out of the 76 entities for further insights. The FTD survey and interactions focused on gaining an in-depth understanding of the ecosystem, including risks and challenges in adoption, governance aspects, and regulatory expectations. The key findings from these surveys and follow-up interactions are summarised below:

3.3.2 Use of AI and Organisational Goals: It was observed from the DoS survey that only 20.80% (127) of 612 surveyed entities were either using or developing AI systems.

Figure No. 3: AI adoption by Supervised Entities

3.3.3 The low number was on account of non-adoption in a majority of smaller Urban Co-operative Banks (UCBs) and NBFCs35. In case of UCBs, no AI usage was reported by Tier 1 UCBs36, while adoption among Tier 2 and Tier 3 UCBs remained below 10%. Among the 171 surveyed NBFCs, only 27% have been using AI in some manner. No adoption was observed among Asset Reconstruction Companies (ARCs). While larger public and private sector banks have greater adoption, it was largely in the form of simpler rule-based models or early-stage exploration of advanced models. This was also corroborated by the FTD survey and interactions, which indicated that AI adoption remained low and limited to larger institutions with simpler models that require lower investment and infrastructure. There is a clear divide between larger and smaller institutions in terms of exploring AI adoption. This is primarily due to capacity constraints, limited business case and infrastructural costs. Surveyed entities indicated that process efficiency improvement, improved customer interface and assistance in decision making were the primary organisational goals for adopting AI. In most instances, the use of AI was limited to simple applications such as predictive analysis, lead generation and chatbots for customer queries.

3.3.4 Complexity of the Models Deployed: Most respondents largely relied on simple rule based non learning AI models and moderately complex ML models, with limited adoption of advanced AI models. In interactions with these entities, it became clear that simpler models were preferred due to ease of implementation, compatibility with legacy systems, and greater control and explainability. There was a preference towards cloud-based deployments for lower cost, scalable solutions and expansion of digital services, with 35% respondents using the public cloud.

Figure No. 4: Model Complexity
 
Figure No.5: Deployment Environment

3.3.5 AI Applications and Areas of Deployment: Out of the total 583 AI applications in production and under development, the most common applications were in customer support (15.60%), sales and marketing37 (11.80%), credit underwriting38 (13.70%), and cybersecurity39 (10.60%). These functions typically involved lower risks, structured flows, predictive outcomes and easier implementation, making them conducive to early AI implementation. The cybersecurity applications mostly included third-party enterprise solutions that were easier to integrate with existing systems. In contrast, applications under development included internal administrative tasks and coding assistants.

Figure No. 6: Use cases of AI tools in Financial Institutions

3.3.6 From the FTD survey, it was observed that there was an increased interest in Gen AI. Out of the 76 entities, 67% were exploring at least one Gen AI use case. However, from the interactions with CTOs/CDOs, it was observed that most use cases were in an experimental phase and limited in scope (such as internal chatbots for employee productivity and basic customer support). Entities were reluctant to explore customer-facing financial service use cases, due to concerns around the sensitivity of the data as well as a lack of explainability and bias.

3.3.7 Inclusion-Oriented Use Cases: During the interactions held by FTD, entities suggested that AI has the potential to expand the reach of financial services to the underserved and unserved population through solutions like alternate credit scoring, multilingual chatbots, automated KYC, and agent banking. There were, however, bottlenecks such as sparse data, financial literacy gaps, cost and RoI.

Figure No. 7: AI for Financial Inclusion

3.3.8 Frictions in AI Adoption: The respondents cited several barriers to wider AI adoption that included the AI talent gap, high implementation costs, lack of high-quality data for model training, insufficient access to computing power, and legal uncertainty. Smaller entities, particularly those with resource constraints, highlighted a need for low-cost environments where they could securely experiment before deploying their use cases.

Figure No. 8: Major challenges for AI adoption in %

3.3.9 With the exception of large banks and NBFCs, most of the entities were focused on use cases that provide a short-term return on investment. Their apprehensions included the concern that their investments in AI could become obsolete in a short time, considering the pace of hardware evolution, model developments and training parameters. The respondents pointed out that AI applications are not plug-and-play, and require high-quality data, domain-specific customization, and skilled human capital to deliver the desired outcomes.

Figure No. 9: Institutional risk reported by surveyed entities

3.3.10 The major risks that entities identified include data privacy, cybersecurity, governance and loss of reputation. From the FTD interactions, it was clear that entities were apprehensive about implementing advanced AI use cases owing to the inherent opaqueness and unpredictability of the technology and the governance challenges this entailed. It was also clear that mitigating these incremental risks required focused policy and governance actions.

3.3.11 Internal Risk Mitigation Practices: Differences were also observed between institutional governance and risk mitigation frameworks. Only one-third of the respondents, which mainly comprised large PSBs and Pvt banks, reported having some level of Board-level framework for AI oversight. Only one-fourth of the respondents mentioned having formal processes in place for mitigating AI-related incidents or failures. Some of the entities confirmed that AI risks have been incorporated in the existing product approval and risk management processes, but that specific AI risk management verticals had not yet been implemented as they were still in the early adoption stage. Most respondents did not mention efforts at training employees and increasing their awareness of AI risks, which may hinder organizational readiness to handle evolving AI risks.

3.3.12 Policies for Data Management: Most entities did not have a dedicated policy for training AI models. Key aspects of the AI data lifecycle, such as data sourcing, pre-processing, bias detection and mitigation, data privacy, storage and security, were being handled in a fragmented manner. The entities relied on existing IT, cybersecurity and privacy policies for this. Most entities have not put in place the sort of data lineage and data traceability systems which are critical for accountability and model reliability. Many said that it was difficult to access domain-specific, high-quality, structured data, especially from legacy systems, and noted that there was a need to put in place data governance frameworks.

3.3.13 Monitoring Model Performance: Of the 127 entities that reported use of AI, only 15% admitted to using interpretation tools like SHAP40 or LIME,41 and only 18% maintained audit logs. Although 35% validated for bias and fairness, such practices were limited to the development stages and did not extend to deployment. While 28% rely on human-in-the-loop mechanisms, far fewer had bias mitigation protocols (10%), and regular audits (14%). On the safeguards around AI/ML model performance, while 37% of respondents reported periodic model retraining, only 21% monitored for data or model drift, and just 14% conducted real-time performance monitoring. The interactions revealed that a robust governance framework, close collaboration between functional teams and clear accountability across the ecosystem were crucial for the implementation of AI applications.

3.3.14 Building Capacity and Skill: A few organisations had initiated AI skill-building through internal training programs, collaborations with academic institutions like IITs, partnerships with industry leaders, workshops, and certification courses focused on AI, GenAI, and related technologies. Some have established AI Centres of Excellence, conducted hackathons, and engaged external experts to upskill employees. Even so, skill development remains a critical challenge with insufficient talent pools and fragmented capacity building efforts. Many entities pointed out that they needed to rely on self-learning given the lack of comprehensive industry-wide capacity development and collaborative learning programs. Respondents also highlighted the need to significantly boost customer awareness and deepen their understanding of AI-driven use cases to ensure more effective adoption and engagement.

3.3.15 Expectations from Regulators and Policy Makers: 85% of the respondents (68) to the FTD survey expressed the need for a regulatory framework. The interactions revealed that guidance on critical issues such as data privacy, algorithmic transparency, bias mitigation, use of external LLMs, cross-border data flow, and a proportional risk-based approach may help ensure responsible AI adoption.

3.3.16 This chapter analysed the evolving policy landscape pertaining to the use of AI in financial services. It also captured insights and expectations from the ecosystem as they navigate the opportunities and challenges of AI adoption. In developing its internal position on AI, India must ensure that it aligns itself with global developments in AI while at the same time safeguarding its national interests. This will allow it to actively participate in international fora where these safeguards and regulatory frameworks are being developed at a global scale, but do so in a manner that is consistent with its national strategic goals. To that end, while India can align with the risk mitigation measures that most countries around the world have adopted, it should do so with a clear eye on making sure that in doing so, it does not deny itself the ability to use this technology to accelerate development. Together, these perspectives have provided the Committee with a well-rounded frame of reference to formulate its framework and recommendations in the Chapter 4.

Chapter 4 – Building a Responsible and Ethical AI Framework

The preceding chapters have laid out the evolving AI landscape in the financial sector. Drawing on survey findings and stakeholder consultations, the Committee assessed the extent of AI adoption across financial institutions and gained an understanding of some of the frictions in pursuing innovation and adoption by entities. This was followed by an exploration of AI’s transformative potential, as well as the risks associated with AI deployment. A review of global developments provided further insight into how other jurisdictions are approaching the governance of AI in financial services.

Against this backdrop, it is important to reiterate the core objectives that motivated the constitution of this Committee: the need to design a forward-looking framework that will support innovation and adoption of AI in India's financial sector in a responsible and ethical manner. While actionable and practical recommendations are essential, the Committee concluded that it is equally, if not more important, to lay down a set of overarching principles that must stand the test of time, serving both as a strong foundation and a guiding light for responsible AI innovation in the financial sector. These principles, together with the actionable recommendations, must be firmly anchored in the most critical element in financial services, i.e., trust.

4.1 Trust as the Cornerstone

4.1.1 Trust is the foundation of all regulated ecosystems. Consumers must trust that the system is fair, accountable, and designed to protect them. REs must trust in the clarity, consistency, and certainty of policies.

4.1.2 The cost of inaction is substantial. The erosion of trust not only undermines consumer confidence but also poses the risk of systemic shocks, fraud, litigation, and reputational damage. Trust, once lost, is difficult to regain. It becomes even more critical to maintain trust when people’s money and livelihoods are at stake. As AI becomes increasingly embedded in financial services, it is imperative that it should reinforce, not undermine, trust.

4.1.3 Many find AI systems opaque and worry that autonomous decisions made by these systems will be inexplicable and have unintended consequences. They are concerned about the unethical sourcing of data and that these systems could be used for harmful activities. The path to trust requires not only transparency and safety but also a focus on ethical AI adoption that respects rights and upholds fairness. Unless it is trusted, no technology, no matter how powerful, will be adopted. Trust must be the guiding force behind all actions taken across the entire AI lifecycle. It must be viewed not as a regulatory burden but as a powerful enabler which will accelerate adoption, build confidence, and strengthen India’s competitive edge.

4.1.4 This brings up the issue of whether a framework is necessary to ensure trust in AI or if we can achieve this without regulatory policy. Advocates for minimal regulation argue that a less restrictive environment fosters innovation and transformative improvements in financial services. However, AI can bring with it significant risks that can only be mitigated by having an appropriate framework.

4.1.5 Policymakers should not have to choose between one or the other but instead strike a balance between them. The Committee’s overarching objective is therefore to establish a forward-looking and balanced framework for responsible and ethical AI adoption. A framework where AI-driven technological innovation reinforces trust in the financial system, where regulatory safeguards preserve it, and which remains agile enough to evolve with technological advancements.

4.2 Enablers and Considerations for Advancing Trustworthy AI

4.2.1 Having established trust as the foundation for AI adoption in the financial sector, it is imperative to identify the key areas where facilitative action can accelerate progress towards this objective. Drawing from stakeholder consultations, industry surveys, and international studies, the Committee has identified two broad categories:

  1. The first, Core Enablers for AI Innovation, refers to the foundational capabilities and infrastructure required to support the broader ecosystem to develop, deploy, and scale AI technologies. This includes improving the availability of high-quality data, bridging infrastructural gaps such as inadequate computational resources, building capabilities for training, testing, and fine-tuning, and strengthening institutional and investment support.

  2. The second, Challenges for Responsible and Ethical Adoption of AI, relates to risks arising from the use of AI technologies. These include concerns around the technology, such as lack of explainability, bias, and hallucinations; around data, such as privacy, security, and control; around governance, such as managing third-party dependencies, ensuring clear accountability and liability; and around systemic risks, such as consumer protection, cybersecurity, model correlation and concentration.

4.2.2 These two categories illustrate the dual challenge facing policymakers and stakeholders, i.e., the need to build an enabling ecosystem that fosters AI innovation, while simultaneously ensuring that AI does not cause harm. Addressing both issues is critical to building a trustworthy AI ecosystem.

4.3 The Seven Sutras - Guiding Principles

4.3.1 The Committee believes that the way ahead must be anchored in a principle-based framework. To this end, the Committee has formulated 7 Sutras - a set of foundational principles that will guide the development, deployment, and governance of AI in the financial sector.

Sutra 1: Trust is the Foundation

  • Trust is non-negotiable and should remain uncompromised

In a sector that safeguards people’s money, there can be no compromise on trust. AI systems should enhance and not erode public trust in the financial system. When consciously embedded into the essence of AI systems and not treated as a by-product of compliance, trust can be a powerful catalyst for innovation. It is essential to build trust in AI systems and build trust through AI systems.

Sutra 2: People First

  • AI should augment human decision-making but defer to human judgment and citizen interest

While AI can help to improve efficiency and outcomes, final authority should rest with humans, who should be able to override AI, especially for societal benefit and human safety. Citizens should be made aware of AI-generated content and be informed when interacting with AI systems. Keeping human safety and interest at the core makes AI trusted.

Sutra 3: Innovation over Restraint

  • Foster responsible innovation with purpose

AI should serve as a catalyst for augmentation and impactful innovation. Responsible AI innovation, that is aligned with societal values and aims to maximise overall benefit while reducing potential harm, should be actively encouraged. All other things being equal, responsible innovation should be prioritised over cautionary restraint.

Sutra 4: Fairness and Equity

  • AI outcomes should be fair and non-discriminatory

AI systems should be designed and tested to ensure that outcomes are unbiased and do not discriminate against individuals or groups. While AI should uphold fairness, it should not accentuate exclusion and inequity. AI should be leveraged to address financial inclusion and access to financial services for all.

Sutra 5: Accountability

  • Accountability rests with the entities deploying AI

Entities that deploy AI should be responsible and remain fully accountable for the decisions and outcomes that arise from the use of these systems, regardless of their level of automation or autonomous functioning. Accountability should be clearly assigned. Accountability cannot be delegated to the model and underlying algorithm.

Sutra 6: Understandable by Design

  • Ensure explainability for trust

Understandability is fundamental to building trust and should be a core design feature, not an afterthought. AI systems must have disclosures, and the outcomes should be understood by the entities deploying them.

Sutra 7: Safety, Resilience, and Sustainability

  • AI systems should be secure, resilient and energy efficient

AI systems should operate safely and be resilient to physical, infrastructural, and cyber risks. These systems should have capabilities to detect anomalies and provide early warnings to limit harmful outcomes. AI systems should prioritise energy efficiency and frugality to enable sustainable adoption.

Figure No. 10: The 7 Sutras

4.3.2 The 7 Sutras operate as an interconnected whole, reinforcing one another to form a robust framework for the responsible innovation and adoption of AI. True to the Sanskrit origin of the word sutra, meaning “thread,” these principles are to be woven through the entire lifecycle of AI systems. They are the bedrock of the FREE-AI framework and apply to every institution that seeks to build, deploy, or govern AI in the Indian financial sector. They are not abstract propositions but are actionable principles that should be integrated into policies, governance frameworks, operational protocols, and risk mitigation systems of institutions.

4.4 Principles to Practice - Recommendations

4.4.1 With the 7 Sutras as the guiding light, this section sets out actionable, structured, and forward-looking recommendations under the FREE-AI Framework.

4.4.2 The responsible deployment of AI within the financial sector calls for a dual focus approach - one that both fosters innovation and mitigates risks. Encouraging innovation and mitigating risks are not competing objectives, but complementary forces that must be pursued in tandem. Accordingly, the recommendations have been grouped into two complementary sub-frameworks, each addressing distinct but interrelated objectives as follows:

4.4.3 The first is the Innovation Enablement Framework that unlocks the transformative potential of AI in financial services by enabling opportunities, removing barriers, and accelerating AI adoption and implementation in a responsible manner. The three key pillars under this framework are:

  • Infrastructure – Building the infrastructure needed to support AI innovation.

  • Policy – Putting in place agile, adaptive policy and regulatory architecture to encourage responsible AI adoption.

  • Capacity – Promoting human skill development and institutional capacity to harness AI safely and effectively.

4.4.4 The second is the Risk Mitigation Framework, which is designed to mitigate the risks of integrating AI into the financial sector. The three key pillars under this framework are:

  • Governance – Establishing robust governance structures in respect of AI-based decisions and actions.

  • Protection – Ensuring strong safeguards for protection from harms.

  • Assurance – Instituting mechanisms for continuous validation and oversight of AI systems.

Figure No. 11: Complementary Sub-Frameworks

4.4.5 To bring the FREE-AI Framework to life, the Committee makes 26 targeted recommendations. These recommendations are a strategic blueprint to build AI responsibly and govern it wisely.

Innovation Enablement Framework

4.4.6 In order to unlock the transformative potential of AI, we need an enabling environment where responsible innovation can flourish. This requires foundational infrastructure, agile policies, and human capability. The following recommendations are designed to enable AI innovation and are presented across three distinct pillars: Infrastructure, Policy and Capacity.

Infrastructure Pillar

4.4.7 Innovation is impossible without foundational infrastructure to support it. In the context of AI in finance, this includes data ecosystems, compute capacity, and public goods that can power experimentation. While MeitY is leading the national efforts to make hardware and compute capacity more accessible, the recommendations under this pillar are focused on building the infrastructure ecosystem that the financial sector needs to unlock and encourage innovation.

4.4.8 Equitable Access to High Quality Data: Most of the data in the financial sector is fragmented across institutions, registries, and platforms. Data availability is asymmetric, i.e., large incumbents have access to huge datasets that smaller REs lack. It is often stored in non-standard formats, making it difficult to use. As a result, substantial time and effort has to be spent collecting, cleaning, and transforming data before it can be used in AI.

4.4.9 To address these challenges, there is a need to establish a publicly governed data infrastructure (such as a data lake) which would aggregate and standardise diverse datasets from across the financial ecosystem. This would serve as a valuable resource for responsible AI innovation. This data infrastructure can leverage the AI Kosh – the India Datasets Platform being established as a Digital Public Infrastructure (DPI) under IndiaAI Mission by MeitY – in order to leverage datasets from other domains, along with financial datasets. To ensure interoperability, the data infrastructure will enforce consistent metadata, formats, and validation standards. It would democratise access to innovation by making it possible for large and small players, FinTechs and technology entities to build trustworthy AI services.

4.4.10 The financial sector data infrastructure must ensure that personal and confidential data are protected. This would call for the use of privacy-enhancing technologies (PETs), anonymization, and data aggregation as applicable. Additionally, due care must be taken to respect intellectual property rights when using proprietary datasets. Additional conditions can be applied to ensure that models that use public data must be released as open source. Access to the data infrastructure must be governed by clear frameworks, in line with the National Data Sharing and Accessibility Policy (NDSAP), 2012, that ensure that entities can only use the data subject to usage obligations and accountability norms. To ensure transparency, accountability, and long-term credibility, the data infrastructure should further be governed by a neutral, multi-stakeholder arrangement among the financial sector regulator(s), industry, and academia and should be periodically updated.

4.4.11 Enabling Innovation Through Safe and Controlled Experimentation: AI innovators need safe spaces within which they can conduct controlled experiments before real-world deployment. An AI Innovation Sandbox can offer potential innovators (including FinTechs, REs and TSPs) shared infrastructure (such as computational resources, foundation models, quality data) that they can use to build, refine, and validate their AI models, products and solutions before deployment. Supervisory authorities and financial institutions can examine how these models, products and solutions behave in the sandbox before they are rolled out.

4.4.12 The sandbox being proposed in this Recommendation is different from existing financial sector regulatory sandboxes that permit live experimentation with real users in controlled environments. The AI Innovation Sandbox will provide infrastructural support for experimentation, model development, and the assessment of technical readiness without any regulatory relaxations. Access to the AI Innovation Sandbox should be subject to defined participation timelines, conformity with financial sector use cases, responsible platform usage, strong security guidelines, and clear exit criteria. This does not preclude AI-related applications from being a part of the regular Regulatory Sandbox, which will continue to offer regulatory relaxations etc., under its current framework.

4.4.13 The Reserve Bank of India is well-positioned to operationalise this initiative, either itself or through its subsidiaries like the Reserve Bank Innovation Hub, within the next year. Technical and compute support, such as GPUs and foundational models, could be provisioned through MeitY and the India AI Mission. Safe experimentation is an essential ingredient for innovation and must be offered as a public utility without compromising financial stability.

4.4.14 Addressing the Digital Divide in Access to AI Infrastructure: Many smaller financial institutions lack the cloud infrastructure or investment capacity needed to deploy AI models safely and in a compliant manner. There is a risk that AI adoption becomes concentrated among large, well-resourced institutions, leaving smaller banks, NBFCs, cooperatives, and new entrants at a competitive disadvantage. This could unintentionally widen systemic inequality, slow down financial inclusion efforts, and undermine the trust that AI aims to provide. It is essential to ensure that AI adoption takes place across the length and breadth of the financial sector in an inclusive, equitable, efficient, and sustainable manner.

4.4.15 The Committee recommends the establishment of dedicated plug-and-play ‘landing zones’ for shared AI compute resources that could be offered to smaller entities at affordable rates on a pay-per-use basis. Similar to the cloud infrastructure provided by IFTAS, the RBI IT subsidiary, these ‘landing zones’ could be offered as shared infrastructure facilitated by RBI or similar institutions such as NABARD or Umbrella Organisations for Cooperative institutions. These landing zones must enable robust isolation, ensure that the security responsibilities between infrastructure providers and participating institutions are well defined, and continuously monitor security to ensure safety and confidentiality. To begin with, these landing zones could leverage the GPUs being made available under IndiaAI Mission at an affordable cost. RBI could put in place incentive schemes to ease the cost of adoption for smaller entities.

4.4.16 The Committee believes that other incentives to promote AI adoption should also be considered. These can be either in the form of a model repository for open-source models or incentives for the use of AI models to serve the unserved or underserved. The RBI’s Payment Infrastructure Development Fund (PIDF) model, which has been successful in promoting digital payments, could serve as a guiding framework for such incentives. These incentives could be offered based on clear metrics such as the use of AI to achieve incremental inclusion of new-to-credit customers or for setting up benchmarking services. To further support these efforts in a sustained manner, the RBI may consider allocating an initial indicative sum of ₹5,000 crore as a corpus for contributing towards the creation of shared data and compute infrastructure as public goods and for fostering innovation in the financial sector.

  • A part of the corpus may be directed towards building shared AI infrastructure, including compute and data, to democratise access. Investment in compute infrastructure should also include some that is quantum-based to ensure that its investments in AI are future-proof.

  • Another portion could be used to incubate AI labs in RBIH, academic institutions of excellence, supporting developer–academic collaboration. RBI could also provide grants to create world-class fintech accelerators across India.

  • Select labs could also focus on emerging areas such as AI–Quantum interactions and synergies to future-proof financial sector infrastructure.

4.4.17 In view of the rapidly evolving nature of the sector, an additional sum of ₹1,000 crore per annum may also be considered for the next five years to support additional initiatives, subject to annual review. The investments in these areas must be viewed as long-term strategic initiatives with public good objectives and not be strictly governed by the expectation of returns.

4.4.18 Building AI Models for the Indian Financial Sector: General-purpose Large Language Models (LLMs) that are trained on diverse datasets tend to produce general outputs that do not align with the requirements of the Indian financial sector and do not reflect its diversity. Domain-specific models trained on regulatory documents (RBI, SEBI, IRDAI), financial laws, product structures, and real-world cases should be able to generate responses that are precise, reliable, legally grounded, and actionable. Where appropriate, efforts should be made to also explore the use of non-LLM-based models that may be better suited for certain tasks. Building these kinds of indigenous models will ensure control over model behaviour, data pipelines, and fine-tuning cycles without dependence on foreign infrastructure or exposure to third-party risks. One area in which such models can play a significant role in enabling financial inclusion is by leveraging voice and language models to enable access to financial services through voice in all Indian languages.

4.4.19 In view of this, the question is not whether a sector-specific model is required or not, but rather how these will be developed and maintained. Training and maintaining a sector-grade foundation model calls for adequate compute resources, access to large datasets, and skilled capacity. One way to accomplish this could be if RBI subsidiaries or industry bodies like IBA, SRO FT, etc., can develop indigenous base models and make them available as a public utility for others to fine-tune. Another way could be to encourage the industry to develop such base models themselves and release them as a public good.

4.4.20 AI and Digital Public Infrastructure (DPI): India’s Digital Public Infrastructure (DPI) approach has already significantly advanced digital financial inclusion. However, challenges still remain in reaching unserved and underserved segments as well as those who lack digital capacity. Barriers such as low digital literacy limit the realisation of DPI’s full potential. While DPI has already extended deep into India’s hinterland, AI has the potential to exponentially extend the reach and improve the effectiveness of DPIs.

4.4.21 By purposefully combining AI with DPI, India can build a next-generation layer, i.e., Digital Public Intelligence (DPI 2.0) as an open, innovation-driven, and trust-anchored ecosystem where financial services are tailored, inclusive, secure and impactful. This would allow REs, FinTechs, and innovators to build solutions for those who are not technically capable or who do not understand the language in which digital services are provided. A few illustrative use cases are:

  • Conversational AI-powered financial service delivery can enable voice-led payments/transactions in multiple Indian languages, bridging digital literacy gaps.

  • Combining AI with Account Aggregators can help financial institutions personalise credit and insurance offerings for micro enterprises and informal workers.

  • AI-enabled fraud detection can protect vulnerable users in real time, building trust in digital transactions.

Policy Pillar

4.4.22 In addition to infrastructure, there is a need for a clear, adaptive, and forward-looking policy framework that is aligned with the objectives of using AI in the financial sector. As AI technologies continue to evolve at a rapid pace, financial services policies must remain flexible, proactive, and future-ready. To achieve this, regulators must establish dynamic mechanisms that address emerging risks and foster innovation in a safe and responsible manner.

4.4.23 Adaptive and Enabling Policies by Regulators: Given that AI is a new technology, there may be a need to revisit some of the existing policies to address the new risks that AI poses and unlock restrictions that may come in the way of innovation. There is also a need for periodic assessment to ensure that, as AI continues to evolve, the policies remain relevant and comprehensive to address the incremental needs. In cases where existing regulations already address AI aspects, regulators may need to guide REs as to how existing regulations will apply to AI. Where existing regulations fail to adequately cover AI-specific risks, review and amendments of guidelines should be considered. To illustrate, some of the AI-specific clarifications and enhancements in select RBI Master Directions have been provided in Annexure IV for reference.

4.4.24 In response to the evolution of AI, emerging risks, best practices, and international/national developments, Regulators should formulate a sector-wide AI policy framework anchored in the Committee’s 7 Sutras that should serve as a living document that regulators periodically review and update. These should be viewed as the minimum baseline standards for AI adoption in the financial sector. By anchoring the policy framework in the Sutras, while having the flexibility to refine or expand, regulators can help foster a safe and inclusive environment for AI in financial services. Further, to provide greater clarity and enable responsible innovation across the financial sector and the broader FinTech ecosystem, regulators such as the RBI may consider issuing a comprehensive and unified AI Guidance. This may cover clarifications on existing guidelines, amendments and incremental aspects, which would consolidate AI-specific expectations and serve as a single point of reference for entities aiming to design, develop, and deploy AI solutions.

4.4.25 Leveraging AI to Accelerate Affirmative Action: A one-size-fits-all framework risks either stifling AI innovations or inadequately protecting vulnerable users from harm. Meaningful financial inclusion of the unserved or underserved population calls for a calibrated, progressive approach that promotes financial inclusion and protects financial stability.

4.4.26 AI-driven lending models for small-ticket loans (e.g., under ₹1 lakh) have the potential to onboard first-time borrowers and underserved communities into the formal financial system. However, current compliance expectations, particularly around AI model validation and supervisory obligations, can act as a deterrent to innovation. Drawing from earlier examples, such as the introduction of BSBDA Small accounts with simplified KYC, regulators should encourage AI-powered credit and other inclusion-focused offerings, particularly for low-ticket size use cases. This could take the shape of less onerous compliance obligations while ensuring that the basic tenets of fairness and accountability are met. Financial service providers working to ensure meaningful financial inclusion should be encouraged to innovate without fear of regulatory/ supervisory action. AI can play a pivotal role in advancing affirmative action by breaking language barriers through multilingual interfaces and enhancing accessibility for Divyaang through assistive technologies.

4.4.27 A progressive and principle-based framework for financial inclusion should be built on the following three planks:

  • Fostering Innovation: Institutions should be encouraged to deploy AI for inclusion, on the assurance that compliance obligations would be proportionate.

  • Safeguarding the Vulnerable: AI models used for these purposes must embed protections to ensure that excluded communities are not just onboarded but are genuinely included and treated fairly. Decisions should be sufficiently explainable to ensure that no discrimination, either direct or indirect, occurs.

  • Addressing Provider Misuse: Clear guardrails must be put in place to prevent misuse by providers, predatory lending, hidden charges, and other such discriminatory practices under the guise of using AI.

4.4.28 Liability for AI-Driven Financial Services: Balancing Accountability and Responsible Innovation: Legal liability is typically presented in a binary manner, i.e., those responsible for harm are liable under a direct cause and effect relationship. However, AI systems are inherently probabilistic, with outputs that are often non-deterministic. This makes it challenging to apply this traditional, rigid framework of liability.

4.4.29 Since customer protection is non-negotiable, the RE must remain fully responsible for compensating losses or damages to consumers. However, a graded approach to supervisory action would help encourage AI innovation. To illustrate, if a RE has adhered to prescribed safeguards, such as comprehensive incident reporting, conducting Root Cause Analysis (RCA), regular red teaming, independent audits, and transparency, then the first instance of a failure should not automatically trigger full scope supervisory action. Instead, supervisors should allow the RE a reasonable opportunity to take corrective action. If the RE identifies the issue and takes corrective measures to mitigate similar harms, this proactive remediation should be acknowledged. If, however, the RE repeatedly fails to address identified issues or neglects necessary safeguards beyond an initial corrective measure, then full supervisory action, including penalties, could be applied, considering the severity of individual cases.

4.4.30 The core philosophy of this approach is to ensure that genuine AI usage is not penalised for every error or failure, as this could stifle innovation and adoption. A rigid liability framework that punishes every mistake may result in developers excessively constraining AI’s capabilities, undermining its potential for creating meaningful and innovative solutions. A tiered risk-based liability model, where the REs have the chance to rectify issues upon notification, would encourage responsible innovation. Importantly, this exemption should be conditional and must not be taken for granted. It should not apply in cases of repeated violations, recurring breaches, or gross negligence.

4.4.31 Dedicated AI Institutional Framework for Financial Services: Given the pace and complexity of AI developments in financial services, regulators need to continuously engage with developments so that they can adapt regulatory frameworks to address evolutions in technology. A multi-stakeholder committee anchored within the regulatory ecosystem that serves as a bridge between regulators and the broader ecosystem will ensure that policies are suitably responsive to technological advancements.

4.4.32 The Committee recommends the establishment of a dedicated Standing Committee to provide continuous strategic guidance on the impact of AI across the financial ecosystem. This Standing Committee should include a mix of internal RBI representation, external experts, academicians, technologists, legal professionals and financial sector representatives. This would enable the regulator to keep up to date with advances in AI and proactively evaluate the continued effectiveness of existing guidelines. The Committee should be appointed for a fixed term and can be dissolved unless an extension is warranted based on the maturity of AI adoption in financial services.

4.4.33 In addition to the Standing Committee, the creation of a dedicated institutional framework within the financial sector is needed to continuously assess AI-related risks, support cross-sectoral coordination, issue financial sector–specific standards, audit benchmarks, and guidance to promote responsible innovation. This institution should operate under a hub-and-spoke model, serving as the sectoral spoke aligned with the broader national-level AI Safety Institute (AISI).

Capacity Pillar

4.4.34 No amount of infrastructure investments and enabling policies will catalyse innovation if there are capacity and skill constraints within the ecosystem. In order to effectively harness AI in finance, individuals, teams, and institutions need to be equipped with the knowledge, skills, and mindset necessary to encourage innovation. To create an innovation-driven ecosystem, the sector must prioritize capacity building at every level, embedding AI competence across teams, ensuring leadership is equipped with the necessary strategic oversight capabilities, and promoting a culture of continuous learning and knowledge sharing.

4.4.35 Building AI Capacity and Strengthening Responsible AI Governance Competencies within REs: Decision makers at all levels in REs need to be equipped with a sufficient understanding of the strategic, regulatory, and ethical dimensions of AI. As financial institutions integrate AI into critical processes, from credit underwriting and risk assessment to fraud detection and customer interaction, the oversight and direction provided by the Board and top management will become central to ensuring safe and trustworthy outcomes. At the same time, it is equally important for the broader workforce, particularly those involved in the development, deployment, and day-to-day management of AI systems, to be equipped with the appropriate functional and operational skills.

4.4.36 REs should prioritise capacity building initiatives aimed broadly across the entire workforce, from the Board level down to anyone in the organisation who uses AI. The AI Competency Framework for public sector officials, developed under the IndiaAI Mission by MeitY can act as a reference framework. Institutions should also be encouraged to invite external AI experts into Board sub-committees or advisory roles, particularly when designing and deploying high-impact or high-risk AI systems. Where feasible, Boards may consider inducting members with specific AI governance expertise. It is important to distinguish AI governance expertise from general IT skills. While IT experts provide infrastructure oversight, AI experts bring specialised knowledge in the application of AI technologies, particularly in a financial sector context. Given the challenges of immediately sourcing qualified AI experts, a flexible glide path of two to three years would allow institutions to embed these competencies over time. Smaller financial institutions may be supported by SROs, industry bodies, academia partnerships and ecosystem collaborations.

4.4.37 Collaboration may be encouraged between financial institutions, training providers, EdTech platforms and academia. AI technology entities are to develop specialized training programs to equip staff with new technical skills, but also build awareness of AI-related risks that could affect their day-to-day work. To help strengthen the training capabilities, educational institutions of excellence such as IITs, IIMs, etc., can develop and provide tailored course content on AI in finance. Scalable and inclusive capacity-building models and programs must also be developed to reach a wider base of the workforce, particularly in smaller institutions and rural branches.

4.4.38 Developing Capacity for Financial Sector Regulators and Supervisors: Regulators and supervisors must also develop an understanding of AI technologies, their innovation potential and the ethical challenges they pose. Without it, regulators may inadvertently curtail innovation, issue policies, or adopt supervisory approaches that either overlook critical challenges or fail to provide appropriate safeguards. This gap could result in ineffective oversight, regulatory blind spots, or missed innovation opportunities. To address this challenge, regulators and supervisors must strengthen their institutional capacity through structured and continuous training focused on the evolving landscape of AI, which is expected to ensure that regulatory responses and supervisory oversight remain relevant and proportionate to the dynamic nature of AI deployment in financial services. RBI may consider establishing an AI institute for the financial sector to support capacity building for regulators and supervisors. The AI institute should also conduct industry-training programmes and research activities on emerging AI trends, thereby enabling more responsible AI adoption across the broader financial ecosystem.

4.4.39 Establishing a Framework for Sharing Best Practices and Lessons on AI Use Cases and Adoption: Once AI innovation has been successfully catalysed across the length and breadth of the financial sector, it will be important to put in place a structured framework to share experiences and lessons, opportunities to replicate success, avoid common pitfalls, and identify emerging risks. Regular workshops, policy dialogues, and discussions will keep the sector updated on new developments and opportunities for AI adoption. A voluntary and industry-driven framework will make it possible for the sector to learn from each other’s experience on what works, what doesn’t, and what warrants regulatory scrutiny, while at the same time, positioning India as a global hub for AI-driven financial innovation.

4.4.40 Fostering Responsible Innovation through Recognition or Rewards: Another way to build capacity is to encourage innovation and experimentation by putting in place carefully designed recognition and incentive frameworks. This could include initiatives such as an annual ‘AI in Finance Award’ to recognise exemplary AI innovations across categories like financial inclusion, customer service, fraud detection, AI compliance toolkits, etc. Regulators and industry bodies could institute periodic ‘AI Challenge Grants’ or ‘AI Innovation Prizes’ to incentivise the development of cutting-edge AI solutions. By fostering competitive innovation, especially among non-regulated entities such as start-ups and smaller firms that may lack visibility and resources, it will encourage these organisations to focus on internal capacity building.

Risk Mitigation Framework

4.4.41 While it is important to enable AI innovation, one cannot lose sight of the risks that could arise as AI starts to get increasingly integrated into the financial sector. To this end, it is just as important to put in place a risk mitigation framework that implements the safeguards necessary for ensuring that AI is deployed in a safe and responsible manner. The following recommendations are designed to ensure that AI risks are managed and mitigated appropriately and are presented across three distinct pillars: Governance, Protection and Assurance.

Governance Pillar

4.4.42 Innovation thrives when it operates within a framework of transparent and accountable governance structure. Governance serves as the backbone of any AI-related risk management strategy, ensuring that all AI initiatives align with regulatory expectations, ethical principles, and business objectives.

4.4.43 Establish a Board-Approved AI Policy within REs: AI adoption in financial institutions often takes place without a consistent organisational stance on what constitutes responsible or ethical use. In the absence of a formal policy, different teams within the same organisation may proceed with different interpretations as to what constitutes acceptable risk. This could lead to fragmented implementation, blind spots, and consumer harm. It also risks leaving the board and senior management unaware of the risks or reputational consequences of their use of AI.

4.4.44 Just as financial institutions have board-approved policies on credit, cybersecurity, or outsourcing, they should put in place a board-approved AI policy that explicitly articulates the institution’s position on AI governance, ethics, and accountability that is aligned with its values, obligations, and risk appetite. The policy should also include a clear risk classification framework for AI use cases, categorizing them as low risk, medium risk, or high risk depending on factors such as impact on customers, criticality, and potential for harm. An indicative classification could be as follows: Low-risk use cases may include internal applications such as document summarisation, email classification, etc., where the outcomes have limited impact. Medium-risk use cases could involve customer-facing tools like chatbots, fraud detection systems, etc., where AI is used for preliminary assistance. High-risk use cases would include critical functions such as credit underwriting, autonomous AI systems that handle customer interactions, make financial decisions, or move customer funds, where errors could have significant consequences for customers or the financial system. Importantly, REs must periodically review and update these classifications to ensure they remain relevant and responsive to the evolving situations.

4.4.45 It should be the responsibility of the Risk Management Committee or similar body to identify, assess, and mitigate AI-related risks and integrate them into the institution's overall risk mitigation framework. Additionally, it could consider putting in place an AI Adoption Committee or leveraging any existing body tasked with technology adoption to bridge functional teams across business, risk, compliance, and technology departments, ensuring that AI innovation and adoption are cross-departmental and well managed. All functionaries responsible for risk must be well equipped to explicitly incorporate AI-related risks into the organization's risk mitigation framework.

4.4.46 The use of third-party, or off-the-shelf AI tools (e.g., generative AI applications) for official purposes, such as drafting documents, report summarisation, data analysis etc., should be governed by the policies of the organisation. REs must ensure that their internal AI policies are compliant with the broader national AI governance and regulatory frameworks. A draft AI policy template could be prepared by industry bodies/ SROs so that smaller entities that may not have the skillset to develop one from scratch could adapt it to suit their specific organizational needs. A suggested outline of a Board-approved policy on AI has been provided in Annexure V for reference.

4.4.47 Governing the AI Data Lifecycle: High-quality data is key to trustworthy and effective AI systems. However, weak internal controls relating to access, usage, and storage of data could amplify biases, reduce performance, and result in unreliable outcomes. Robust governance processes at the institutional level complement national policies by building operational trust and enabling safe AI deployment. Accordingly, establishing robust internal data governance frameworks across the entire data lifecycle becomes paramount. From the point of data collection to its final deletion or archival, each stage must be governed by clear internal policies. Data used for AI applications must be relevant, fairly representative, and ethically sourced. Weak controls at any stage, whether due to poor quality checks or failure to adhere to consent obligations, can undermine the integrity of AI systems and expose institutions to reputational, legal, and operational risks. REs should put in place guardrails, especially when using open source or external AI models, to ensure that sensitive customer and institutional data remains within secure environments under the control of the institution. The Digital Personal Data Protection (DPDP) Act provides overarching principles for data protection and privacy and REs are obligated to adhere to DPDP Act provisions and operationalise responsible data management.

4.4.48 Establishing an AI System Governance Framework for Safe and Compliant AI Development: AI system governance refers to the structured oversight of AI models and systems, including both conventional AI models and increasingly autonomous AI systems, supported by clear policies, roles, and controls. Robust model governance is critical to ensuring the reliability, safety, and accountability of AI systems. REs must implement appropriate governance mechanisms across the entire AI model lifecycle, covering model design, development, deployment, and decommissioning. REs should maintain a model inventory and documentation that records essential details, including objectives, design features, usage context, performance benchmarks, intended outcomes, etc. Models, whether developed internally or sourced externally, should undergo rigorous validation and periodic testing to ensure they perform as intended. REs must put in place mechanisms to detect and address issues such as model degradation, model drift, bias, or unexplained behaviour, with clearly defined fallback mechanisms. Ongoing performance monitoring, internal audits, and red-teaming exercises should be employed to identify and subsequently rectify vulnerabilities. Any errant model behaviour or incidents must be formally recorded and reported through appropriate channels. REs should also establish procedures for the timely winding down or replacement of models that become outdated or non-compliant.

4.4.49 Emerging developments in AI have given rise to increasingly autonomous systems that allow AI applications to independently execute tasks that would otherwise have required human involvement. When these systems are tasked with financial functions such as investment decisions, loan processing, or payment execution, they are able to operate with access to real-world customer assets like bank accounts or financial data. While this presents opportunities for efficiency and scale, it also introduces significant risks. Autonomous AI, even when performing simple individual tasks, can generate complex, unintended consequences if not managed well. REs must use autonomous AI only after establishing clear safeguards and accountability frameworks, supported by well-defined testing protocols and standard operating procedures (SoPs). Consumers should be made to fully understand the consequences before being allowed to use such tools. While exceptions may be considered for the use of autonomous AI in routine or low-risk tasks, human oversight remains a critical factor in medium-risk to high-risk tasks. REs must clearly define the tasks AI can perform autonomously and instances when human oversight is required. REs must remain liable for the actions and outcomes of the autonomous AI systems they deploy, just as they are for other forms of operational or technological risk.

4.4.50 AI Specific Evaluations in the Product Approval Processes for AI-Enabled Products and Solutions: As AI-enabled products and solutions are increasingly used in financial services, there is a risk that existing product approval mechanisms may be inadequate to identify and address AI-specific risks. There is a need to integrate AI-specific evaluations into these approval processes.

4.4.51 AI-specific risk evaluations should address key elements such as fairness, bias, understandability, customer protection, cybersecurity, and compliance across the entire product lifecycle from pre-development to deployment and use. The product approval process should assess the quality of data, exclusion of sensitive attributes, data pre-processing, random sampling of outputs, back testing, subject matter expert review, feedback mechanisms, etc. REs are encouraged to deploy internal AI sandboxes to enable controlled testing and validation of their models before deployment. To ensure objectivity, the product approval evaluation team should be independent from the teams responsible for AI model development and deployment.

Protection Pillar

4.4.52 In an AI-driven financial ecosystem, the protection of data, confidential information and consumer interests is paramount to building trust and resilience. Putting in place these protections will ensure that consumers are not harmed while using AI systems.

4.4.53 Putting Consumers First and Safeguarding the Consumer Experience: The failure to proactively address AI risks not only harms individuals but also erodes public trust in AI innovations. REs need to establish robust, board-approved consumer protection frameworks that focus on transparency, fairness, and provide clear recourse mechanisms.

4.4.54 Consumers must have effective means of grievance redressal with regard to their interactions with AI or decisions made by AI. REs must embed clear and accessible safeguards into all AI-enabled offerings. Consumers must be explicitly informed whenever they are interacting with AI systems and should always have the option to switch to human representatives when they want. Firms should not be allowed to deceive customers by falsely claiming to be using AI. REs should ensure that AI-driven systems operate only through secure and verifiable channels such as verified 1601 series phone numbers for voice interactions, watermarked digital interfaces for online channels, and clearly labelled platforms.

4.4.55 Consumers should be able to escalate any AI-related issues to human representatives through easily accessible and effective processes. REs must launch targeted and activity-based awareness campaigns that inform customers about their rights when interacting with AI, explain how AI is being utilized in financial services, and detail the grievance redressal mechanisms that are available to them. Trust is not built by technology alone; it is earned by putting people first.

4.4.56 Mitigating Cybersecurity Threats: The adoption of AI in financial services introduces new cybersecurity risks. AI models potentially expand the attack surface, exposing institutions to threats such as adversarial attacks, data poisoning, and model manipulation. Malicious actors can use AI to automate phishing, create deepfake frauds, and conduct intelligent cyber intrusions. The use of AI by attackers can significantly reduce the time required to conduct cyberattacks and increase their volume.

4.4.57 REs deploying AI in high-risk use cases or using AI extensively in their products and processes should identify vulnerabilities, adversarial weaknesses, and potential security risks before deployment. Cybersecurity assessments must not be limited to the testing or pre-deployment phase, but instead should be a continuous process even after models have been deployed.

4.4.58 AI also offers powerful tools to strengthen cybersecurity. AI-driven anomaly detection, predictive threat intelligence, real-time intrusion monitoring, and adaptive defence systems can significantly enhance the resilience of financial institutions. Additionally, AI systems should be capable of being terminated instantly if there is a risk of significant harm. Consumers should be educated regarding the potential cybersecurity risks involved in the use of AI.

4.4.59 Red Teaming of AI Models and Applications: A key challenge in AI deployment is that the harm caused is sometimes only visible after it has affected several people. A proactive way to address this problem is structured red teaming, an adversarial testing approach designed to challenge AI systems to reveal hidden vulnerabilities, stress points, and risks. For instance, investigating if the AI model memorises and inadvertently leaks sensitive data such as account numbers or transaction details when queried in unintended ways.

4.4.60 Since red teaming is proactive, it makes it possible for REs to anticipate failures and mitigate them in advance. This strengthens model resilience, prevents cascading failures, and enhances consumer and system-level trust in AI-enabled financial services. REs, which deploy medium-risk and high-risk AI applications, should make red teaming a regular practice conducted at periodic (at least semi-annual) intervals. For low-risk AI applications, red teaming should at least be conducted at the pre-deployment stage. In addition, red teaming should be carried out before all major model updates, after vulnerabilities have been detected, when there has been a change in the operational environment, or in the event of evolving regulatory requirements. Findings from red teaming exercises should be documented and made accessible to the audit/ supervisory teams, along with steps taken to mitigate them. Key insights should be shared as part of broader knowledge dissemination efforts across the ecosystem to support collective risk awareness and capacity building.

4.4.61 Ensuring Business Continuity of AI Systems: Despite robust controls, testing, etc., AI systems can fail. Resilience lies in the rapid detection of issues, transparent remediation, and systemic learning. Institutions must embed AI-specific contingencies within their operational resilience frameworks. Failures fall into two categories: traditional system failures (e.g., server outages, cyber incidents) that can be managed via standard Business Continuity Plans (BCPs); and AI-specific failures, where models remain functional but produce unreliable outputs due to distribution shifts or evolving input-output mappings. For instance, a biased model may continue to deny service to a particular segment. In the case of such AI-specific failures, the challenge often lies in the fact that most models are trained with the assumption that the data encountered during deployment will closely resemble the data used during training. When this assumption fails, the model may continue to run without raising any flags, even as its outputs become increasingly inaccurate.

4.4.62 AI-specific BCPs must go beyond traditional recovery strategies and incorporate fallback mechanisms tailored to AI failure modes. This includes having safeguards and fallback mechanisms such as mandatory human-in-the-loop reviews and continuous model performance monitoring. An AI model should be able to declare itself “unavailable” when it fails and trigger backup processes. Institutions should also conduct regular BCP drills in relation to AI-specific failures, simulating scenarios such as data drift and concept drift and implementing periodic human validation checks on a sample of AI decisions (e.g., 1%) to detect silent model degradation.

4.4.63 AI Incident Reporting for REs and Sectoral Risk Intelligence Framework: AI-related incidents in the financial sector can arise across use cases, often reflecting known failure modes of AI systems, such as bias, lack of explainability, privacy breaches, or unintended actions. For instance, AI models used for credit, loan, or insurance decisions may exhibit bias against specific demographic groups; fraud detection models may be circumvented by novel attack strategies; AI agents may act beyond their intended scope; Chatbots may misinterpret customer inputs and conflict with ethical principles. Such incidents can result in significant financial, operational, or reputational harm. Without structured reporting and analysis, such risks may remain hidden until they cause systemic harm. A tiered incident reporting framework is essential at the entity, sector, and national levels to identify patterns, address vulnerabilities, and prevent recurrence. Inspired by the aviation industry, the financial sector must promptly report incidents as soon as possible. The time within which reporting needs to be done could vary based on severity and system-wide implications.

4.4.64 Regulators should design a system for aggregation of risk data for macro-level insights, possibly via an expanded Emerging Technology (EmTech) Repository, and encourage transparent, non-punitive reporting. At the national level, analysis of such incident data should be channelled into inter-regulatory coordination forums to inform coordinated responses and strengthen sectoral resilience. Reporting what was observed, where and how it went wrong, and what remedial action was taken should be sufficient to enable shared learning. This will also serve as an early warning system and ensure AI adoption remains resilient, inclusive, and grounded in public trust. Where a customer has been adversely impacted, compensation must be provided by the RE, but reporting such incidents should not, by itself, trigger penal action if timely corrective measures have been taken and disclosure is complete. The objective is to foster a culture of early AI incident reporting across the financial sector, so that entities and the broader sector can adapt accordingly. An indicative sample Incident Reporting Form template has been provided in Annexure VI for reference.

Assurance Pillar

4.4.65 The assurance pillar is designed to provide oversight throughout the AI lifecycle. It focuses on monitoring emerging risks and feeding those insights back into both institutional and system-wide responses. The framework addresses key questions such as:

  • Do organisations have visibility over the AI systems in use, and do they provide transparent disclosures to stakeholders? Do policymakers have visibility over sector-wide AI adoption and the potential risks building up?

  • Are there effective controls through proactive and continuous testing and auditing of the AI systems to ensure they behave in line with established principles and guidelines?

  • Is there a clear and fair liability framework that ensures accountability for errors and failures, while also ensuring innovation is not stifled?

4.4.66 Trust in AI cannot be assumed; it must be built and, more importantly, sustained. The assurance pillar is the mechanism through which trust is continually reinforced and upheld.

4.4.67 Creating Visibility – Maintaining an AI Inventory within Institutions and a Sector-Wide AI Repository: A key challenge in assessing AI risks is the lack of clear visibility as to where and how AI systems are deployed within institutions and across the sector. Without a structured and updated view of AI usage, it becomes difficult for institutions and supervisors to assess risk exposure or monitor changes over time.

4.4.68 REs should maintain a comprehensive inventory of AI systems in use across their operations. Among other aspects, the inventory should include information on:

  • AI Models and Algorithms: All AI models in use, including the type of model (e.g., machine learning, deep learning, natural language processing, GenAI), their purpose, and the functional areas they support.

  • Use Cases and Applications: A clear description of how each model is deployed.

  • Dependencies: Third-party providers, cloud service providers, data sources, and any other external components that can influence AI model performance.

  • Risk Categorisation: Assessment of each AI system’s risk level (e.g. High, Medium, Low) based on the board-approved policies.

  • Grievances: A record of the volume and nature of grievances filed in respect of these AI systems and how they were resolved. This includes information as to whether the AI solutions have been modified in response to user complaints.

4.4.69 This inventory should be updated semi-annually and must be readily available for supervisory inspections, audits, and ongoing risk monitoring efforts. Maintaining this AI inventory will give both the REs and supervisors a view of where and how AI/ML models are being used to better categorise risk, improve oversight, and ensure responsible deployment.

4.4.70 To complement this institution-level visibility, a sector-wide AI repository should also be developed to collect and maintain aggregate information on AI models and applications across all REs. The EmTech Repository of RBIH can be leveraged by expanding its scope for this purpose. The repository should capture bare minimum, indicative information such as the types and number of AI models deployed across institutions, high-risk use cases, critical dependencies, incidents of AI model failures, ethical breaches, etc. This can also help in monitoring systemic risks such as model correlation and model herding, which, if left unchecked, can amplify vulnerabilities across institutions and potentially pose broader financial stability risks.

4.4.71 Over time, associations such as IBA or SROs can consider developing a Responsible AI Adoption Score or Index for the financial sector, which could serve as a baseline measure to track the maturity and ethical integration of AI across the sector.

4.4.72 Ensuring Responsible AI through a comprehensive Audit Framework: Audits help to independently confirm that systems are operating as intended and within regulatory boundaries. Unlike traditional systems, AI systems are often non-deterministic, adaptive, and opaque, making it difficult to evaluate whether the output is consistent, fair, and compliant with internal policies. This also makes AI prone to specific risks, such as biases and data drift. In order to cater to this, the audit framework that is put in place needs to be risk-based and proportionate in order to ensure that AI systems operate within the guardrails set by the regulator while still allowing room for innovation.

4.4.73 The audit should aim to verify not only that the system works technically but also that it aligns with the 7 Sutras. An effective AI audit should cover:

  • Input Data Audit: It needs to certify that the data used for training or inference is accurate, unbiased, and collected in conformity with the data regulations.

  • Model and Algorithm Audit: It needs to certify that the model architecture, training methods, and decision logic align with the intended purpose and that the models are resilient against manipulation or misuse that could cause them to act contrary to their stated objectives.

  • Output and Behaviour Audit: It needs to certify that the decisions made by the AI model, such as approving a loan, flagging a transaction, or responding to a customer, are explainable, fair, consistent, and compliant with the applicable guidelines and principles and that there are safeguards in place to ensure these outputs cannot be misused or manipulated by bad actors.

4.4.74 The audit should be tailored to the risk level of each application. For instance, internal AI audits for low-risk use cases (e.g., document summarisation) may be minimal, while audits of high-risk applications (e.g., such as credit decisioning) should be detailed. The audit should also confirm that mechanisms exist to stop, pause or unwind AI-driven processes in a controlled manner in case of malfunction or policy breach. It should also verify the presence of a Business Continuity Plan (BCP) for core AI systems and ensure that human oversight and override mechanisms are available for critical decisions. In cases where the risk is particularly high or where internal expertise may be limited, third-party AI audits by independent experts can provide necessary assurance. Audits should be periodic and evolving, with mechanisms to continuously update audit controls and coverage areas, considering new risks, such as agent-to-agent interactions.

4.4.75 Supervisory audits should also evolve accordingly. Inspection by supervisors should include standardised AI-specific checklists and model risk templates tailored to AI systems, providing clarity on what aspects to audit, how to evaluate performance, and how institutions can demonstrate compliance.

4.4.76 Promoting Transparency through Public Disclosures of AI Use and Safeguards: In order to foster public trust and provide assurance, customers and external stakeholders should have visibility into how AI is being governed and whether their concerns are being acknowledged and addressed. To this end, having AI disclosures in publicly available reports can play a vital role in strengthening confidence among the public and stakeholders. Not only will this help to promote market discipline, it will also nudge institutions towards responsible AI practices. Just as climate risk and cybersecurity disclosures are now part of annual reports and ESG filings, AI disclosures should become a regular feature of REs’ annual reports. The disclosures may contain necessary details regarding AI governance frameworks in place, adoption areas, ethical guidelines adopted, consumer protection measures, complaints and grievances handled, etc.

4.4.77 Enabling Responsible AI Compliance through Standardised Assessment Toolkits: REs may lack standardised, practical mechanisms to demonstrate that their AI systems are performing in line with the 7 Sutras. By making available standardised open-source tools which can evaluate the AI model from different dimensions, such as model accuracy, transparency, fairness, etc., REs will be able to demonstrate compliance.

4.4.78 Regulators should facilitate the development of industry-led AI Compliance Toolkits to help REs validate that their AI models and applications meet regulatory expectations. These toolkits can serve both as a diagnostic as well as a benchmarking mechanism, enabling the validation of key AI risks. The use of the toolkit should be voluntary but strongly encouraged, especially for smaller and mid-sized REs that may lack internal capabilities. The toolkit could be developed and maintained by an industry body or SRO, or a consortium of financial sector participants. Third-party service providers should be encouraged to offer toolkit-based validation services, without regulatory endorsement. Regulators may periodically identify and share best practices to guide the continuous improvement of these toolkits. The toolkits would offer a baseline confidence level but not absolve institutions of their responsibility for end-to-end AI risk management, and are intended to complement, not replace, internal validations or oversight.

4.5 Conclusion - Weaving It All Together

4.5.1 As AI continues to evolve and reshape the financial landscape, it brings with it both transformative opportunities and complex challenges. This report has sought to present a balanced and forward-looking framework of how AI can be responsibly and ethically enabled in the Indian financial sector. At the heart of the FREE-AI framework are the 7 Sutras, the foundational principles which are the living spirit of the framework. The 6 Pillars provide structural balance by enabling innovation as well as mitigating risks. Finally, the 26 Recommendations bring it all to life with specific, implementable steps that translate aspiration into action. The recommendations have been carefully crafted to embody and advance the Sutras. Together, the Sutras, the Pillars, and the Recommendations, forge a progressive path forward for all stakeholders, including regulators, financial institutions, technology service providers, to harness the potential of AI in the financial sector.

Summary of Sutras and Recommendations

 

Annexure I – Interactions with Stakeholders by the Committee


Annexure II – Interactions with Stakeholders by the Secretariat


Annexure III – IndiaAI Mission: Strategy and Status

The IndiaAI Mission is the Government of India’s flagship program to build a cohesive, strategic, and robust AI ecosystem.

The IndiaAI Compute pillar focuses on creating a high-end, scalable AI computing ecosystem to deliver Compute-as-a-Service for India’s rapidly growing AI startups and research community. So far, over 34,000 GPUs have been made available at subsidized rates through the IndiaAI Compute portal, with an additional 4,000+ GPUs expected in the next phase of empanelment. The mission also plans to establish a government-controlled GPU cluster of about 3,000 GPUs to meet sovereign and strategic needs.

The IndiaAI Application Development Initiative (IADI) is designed to foster the development and adoption of at least 25 impactful AI solutions that can drive large-scale socio-economic transformation. The first Innovation Challenge, launched in 2024, wherein thirty applications have advanced to the prototyping phase, with a second round of the challenge set to launch in collaboration with the Ministry of Education.

AIKosh, the IndiaAI Datasets Platform, is envisioned as a unified data platform integrating datasets from government and non-government sources. Launched in beta in March 2025, it currently features over 874 datasets, 207 AI models, and more than 13 development toolkits. The platform has attracted over 265,000 visits, 6,000 registered users, and 13,000+ resource downloads. AIKosh prioritizes data quality scoring, robust search and filtering, Jupyter notebooks for analytics, and secure, permission-based access for contributors.

The IndiaAI Foundation Models pillar underscores the importance of building India’s own large language models (LLMs) trained on Indian datasets and languages, to ensure sovereign capability and global competitiveness in generative AI. A funding model combining grants and equity support has been introduced, offering 40% of compute costs as grants and taking 60% as equity (via convertible debentures). From 506 proposals received, four startups (Sarvam AI, Soket AI, Gnani AI, and Gan AI) have been selected in the first phase to develop India’s foundation models.

The IndiaAI FutureSkills pillar is a cornerstone of the mission’s human capital strategy, aiming to democratize AI education and build a robust talent pipeline across the country. The program will support 500 PhD fellows, 5,000 Master’s students, and 8,000 undergraduates through targeted funding. Research fellowships for PhD scholars are aligned with the Prime Minister’s Research Fellowship, offering support of up to ₹55 lakh per fellow. Over 200 students have received fellowships in the first year, with 26 partner institutes onboarding PhD students. Additionally, more than 570 AI and Data Labs are planned nationwide, with 27 labs already in progress and further approvals granted for ITIs and polytechnics across 27 states and UTs.

The IndiaAI Startup Financing pillar addresses the critical need for risk capital across the entire lifecycle of AI startups, from prototyping to commercialization. This includes the IndiaAI Startups Global program, launched in collaboration with Station F (Paris) and HEC Paris, which aims to support 10 Indian AI startups in expanding into the European market. A call for proposals to establish state-level Centers of Excellence in AI has also received 29 submissions from 21 states and union territories.

Finally, the Safe and Trusted AI pillar seeks to balance innovation with strong governance frameworks to ensure responsible AI adoption. Recognizing India’s diverse social, cultural, economic, and linguistic landscape, this pillar focuses on developing contextualized instruments of AI governance. The first Expression of Interest (EoI) selected eight projects addressing themes such as machine unlearning, bias mitigation, privacy-preserving machine learning, explainability, auditing tools, and governance testing frameworks. A second EoI round, focused on watermarking, ethical AI frameworks, risk assessment, stress testing tools, and deepfake detection, received 400+ applications. Plans are also underway to operationalise the IndiaAI Safety Institute under a hub-and-spoke model to address AI risks and safety challenges in collaboration with research institutions and industry partners.

In addition, India is set to host the AI Impact Summit in February 2026, building on its role as co-chair of the AI Action Summit and continuing its leadership in shaping global AI discussions.


Annexure IV – AI Specific Enhancements in RBI Master Directions


Annexure V – Suggested Outline of Board Policy on AI

This document outlines the aspects that an entity should cover while formulating its Board policy on AI. It may be customised to the organisation’s needs and complexity of use and aligned with the recommendations of the FREE-AI committee report.


References

1) Bank of England (BoE) and Financial Conduct Authority (FCA) (2022), Discussion Paper on Artificial Intelligence and Machine Learning in UK Financial Services

2) Center for Research on Foundation Models (CRFM), (2021), On the opportunities and risks of foundation models, Stanford Institute for Human Centered Artificial Intelligence, Stanford University

3) Department of Economic Affairs. (2024). Report of India’s G20 Task Force on Digital Public Infrastructure. Ministry of Finance, Government of India.

4) Ernst & Young (2025), How much productivity can GenAI unlock in India? The AIdea of India: 2025

5) FSB (2017), Artificial Intelligence and Machine Learning in Financial Services. 1 November.

6) FSB (2024), The Financial Stability Implications of Artificial Intelligence, 14 November.

7) FCA, (2025), FCA allows firms to experiment with AI alongside NVIDIA. Press Release, 9 June

8) Frazier, K. (2024), Selling Spirals: Avoiding an AI Flash Crash, Law Fare publication, 8 November

9) J.P. Morgan (2023), How AI will make payments more efficient and reduce fraud, J.P. Morgan Insights

10) Hong Kong Monetary Authority (HKMA) (2025), HKMA and Cyberport launch second cohort of GenA.I. Sandbox to accelerate A.I. innovation in financial sector. Press Release, 28 April

11) Hu, K. (2023) ChatGPT sets record for fastest-growing user base – analyst note, Reuters, 2 February

12) IBM (2018), AI Fairness 360 (AIF360)

13) IndiaAI (2025), India Takes the Lead: Establishing the IndiaAI Safety Institute for Responsible AI Innovation, 31 January

14) IndiaAI, Nasscom Responsible AI Resource Kit

15) Indian Banks’ Association (IBA) (2025), IBA Technology Survey and Benchmarking Report

16) Infosys (2025), The Infosys Responsible AI Toolkit, February

17) International Organization for Standardization and International Electrotechnical Commission (ISO/IEC) (2023) ISO/IEC 23894:2023 Information Technology – Artificial Intelligence – Guidance on Risk Management

18) ISO/IEC (2023), ISO/IEC 42001:2023 Information technology – Artificial intelligence – Management system

19) ISO/IEC (2022), ISO/IEC 23053:2022 Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML).

20) Microsoft (2021), Responsible AI Toolbox

21) McKinsey & Company, (2024), Scaling gen AI in banking: Choosing the best operating model

22) NITI Aayog (2018), National strategy for Artificial Intelligence, NITI Aayog, Government of India

23) NITI Aayog (2021), Responsible AI for All. NITI Aayog, Government of India

24) OECD (2024), Regulatory approaches to AI in finance, OECD Artificial Intelligence Papers No. 24, September

25) OECD (2019, 2014), AI Principles

26) Pol, R. and Pachisia, A. (2025), ‘Designing India’s AI Safety Institute’, The Hindu, 5 March

27) RBI (2024), Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) in the Financial Sector – Setting up of a committee, Press Release, 26 December

28) SEBI (2025), Consultation Paper on Guidelines for Responsible Usage of AI/ML in Indian Securities Markets, 20 June

29) Stanford University, (2025), Artificial Intelligence Index Report, Stanford Institute for Human Centered Artificial Intelligence.

30) Statista (2023), Global generative AI in finance market size

31) World Economic Forum (WEF) and Accenture (2025), Artificial Intelligence in Financial Services. White paper, World Economic Forum, January


Glossary of Key Terms


1 https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/

2 Stanford: Artificial Intelligence Index Report 2025

3 https://www.mckinsey.com/industries/financial-services/our-insights/scaling-gen-ai-in-banking-choosing-the-best-operating-model

4 https://rbi.org.in/web/rbi/-/press-releases/statement-on-developmental-and-regulatory-policies-4

5 https://reports.weforum.org/docs/WEF_Artificial_Intelligence_in_Financial_Services_2025.pdf

6 https://www.statista.com/statistics/1449285/global-generative-ai-in-financial-services-market-size/

7 https://www.oecd.org/en/publications/regulatory-approaches-to-artificial-intelligence-in-finance_f1498c02-en.html

8 Ernst & Young: How much productivity can GenAI unlock in India? The AIdea of India: 2025

9 https://www.jpmorgan.com/insights/payments/payments-optimization/ai-payments-efficiency-fraud-reduction

10 https://dea.gov.in/sites/default/files/Report%20of%20Indias%20G20%20Task%20Force%20On%20Digital%20Public%20Infrastructure.pdf

11 https://arxiv.org/abs/2108.07258

12 https://www.fsb.org/uploads/P14112024.pdf

13 https://www.lawfaremedia.org/article/selling-spirals--avoiding-an-ai-flash-crash

14 https://www.fsb.org/uploads/P14112024.pdf

15 https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/

16 https://www.oecd.org/en/topics/sub-issues/ai-principles.html

17 https://www.iso.org/standard/77304.html

18 https://www.iso.org/standard/81230.html

19 https://www.iso.org/standard/74438.html

20 https://www.thehindu.com/opinion/op ed/designing indias ai safety institute/article69289911.ece

21 https://indiaai.gov.in/article/india-takes-the-lead-establishing-the-indiaai-safety-institute-for-responsible-ai-innovation

22 Bank of England and FCA – Discussion Paper on Artificial Intelligence and Machine Learning in UK Financial Services (Oct 2022)

23 https://www.infosys.com/services/data-ai-topaz/offerings/responsible-ai-toolkit.html

24 https://indiaai.gov.in/responsible-ai/homepage

25 See https://research.ibm.com/blog

26 https://github.com/microsoft/responsible-ai-toolbox

27 https://www.oecd.org/en/publications/towards-a-common-reporting-framework-for-ai-incidents_f326d4ac-en.html

28 An AI incident is an event, circumstance or series of events where the development, use or malfunction of one or more AI systems directly or indirectly leads to any of the following harms: (a) injury or harm to the health of a person or groups of people; (b) disruption of the management and operation of critical infrastructure; (c) violations of human rights or a breach of obligations under the applicable law intended to protect fundamental, labour and intellectual property rights; (d) harm to property, communities or the environment.

29 https://incidentdatabase.ai/

30 https://www.hkma.gov.hk/eng/news-and-media/press-releases/2025/04/20250428-5/

31 https://www.fca.org.uk/news/press-releases/fca-allows-firms-experiment-ai-alongside-nvidia

32 https://www.niti.gov.in/sites/default/files/2023-03/National-Strategy-for-Artificial-Intelligence.pdf

33 https://www.niti.gov.in/sites/default/files/2021-02/Responsible-AI-22022021.pdf

34 https://www.sebi.gov.in/reports-and-statistics/reports/jun-2025/consultation-paper-on-guidelines-for-responsible-usage-of-ai-ml-in-indian-securities-markets_94687.html

35 For the purpose of this survey, an institution was considered to have adopted AI if it has either deployed or is developing any AI systems at least one use case.

36 UCBs: Tier 1 - All unit UCBs and salary earners’ UCBs (irrespective of deposit size), and all other UCBs having deposits up to ₹100 crore; Tier 2 - UCBs with deposits more than ₹100 crore and up to ₹1000 crore; Tier 3 - UCBs with deposits more than ₹1000 crore and up to ₹10,000 crore.

37 Predictive cross sell/up sell models, Customer lifetime value (CLTV) prediction model, Customer churn prediction model, Lead scoring model (prospective customer conversion), banner generation.

38 Machine learning credit scoring models (personal loans, credit cards), Automated document data extraction (OCR/RPA for loan processing)

39 AI driven threat intelligence platform (e.g., CloudSEK), AI enhanced security monitoring (Extended Detection and Response (XDR) platforms like Trend Micro Vision One), AI based network threat detection (e.g., Darktrace)

40 SHapley Additive exPlanations (SHAP) is a technique used to quantify the contribution of each feature to a model's output by assigning it a specific value based on its impact on the prediction.

41 Local Interpretable Model agnostic Explanations (LIME) is a technique that explains model predictions by creating simple, interpretable models that locally approximate the behaviour of complex machine learning models around a specific prediction.

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Annex 3: Important Domestic Regulatory Measures

1. Reserve Bank of India (RBI)

Date Regulation Rationale
December 31, 2024 Government Debt Relief Schemes (DRS): The implementation of various forms of State Government DRS provides for waiver of debt obligations of targeted segment of borrowers against fiscal support. However, frequent announcement of such schemes may affect the credit discipline and impair future credit flow to such borrowers. The guidelines on Government Debt Relief Schemes address these concerns by laying down the prudential treatment of such exposures by REs and by also providing a model operating procedure which may be adopted while designing such relief measures so that the expectations of all stakeholders involved are aligned. To maintain credit discipline and mitigate moral hazard and prudential concerns.
February 07, 2025 Access of SEBI-registered non-bank brokers to NDS-OM: A new facility, viz., ‘stock broker connect’ was introduced in the NDS-OM platform - an electronic trading for secondary market transactions in Government securities. Under this facility, SEBI registered stock brokers have been permitted to directly access NDS-OM on behalf of their individual constituents/ clients. To facilitate retail participation in Government securities.
February 17, 2025 Government securities transactions between a Primary Member (PM) of NDS-OM and its own Gilt Account Holder (GAH) or between two GAHs of the same PM: Matching of PM-GAH and GAH-GAH trades of the same PM on NDS-OM was permitted, along with guaranteed settlement of such trades. An option to settle reported PM-GAH and GAH-GAH trades of same PM through CCIL was also enabled. To bring uniformity in the trading and settlement norms for all transactions in G-secs.
February 24, 2025 Review and rationalization of prudential norms – UCBs: The Reserve Bank has reviewed the prudential norms for UCBs on credit concentration risk, exposures to sensitive sectors and provisioning for relatively riskier exposures. Key measures include revision in definition of small value loans, rationalisation of aggregate exposure limits for housing loans to individuals and a stricter limit for aggregate exposure to other real estate sector, enhanced monetary ceiling on individual housing loans for Tier-3 and Tier-4 UCBs and extension of the five year glide-path allowed to UCBs to provide for the valuation differential on the Security Receipts held against the assets transferred by them to Asset Reconstruction Companies by additional two years. To allow greater operational flexibility to UCBs without diluting the regulatory objectives.
March 24, 2025 Master Directions – Reserve Bank of India (Priority Sector Lending – Targets and Classification) Directions, 2025: The revised guidelines on Priority Sector Lending (PSL) were issued by the Reserve Bank after a comprehensive review along with feedback from stakeholders. They include the following major changes: (i) enhancement of several loan limits, including housing loans for enhanced PSL coverage; (ii) broadening of the purposes based on which loans may be classified under ‘Renewable Energy’; (iii) revision of overall PSL targets for UCBs to 60 per cent of Adjusted Net Bank Credit or Credit Equivalent of Off-Balance Sheet Exposures, whichever is higher; (iv) expansion of the list of eligible borrowers under the category of ‘Weaker Sections’, along with removal of the existing cap on loans by UCBs to individual women beneficiaries. To facilitate better targeting of bank credit to the priority sectors of the economy.
May 07, 2025 Policy Statement - Framework for Formulation of Regulations: The framework for formulation of regulations establishes a standardised, transparent process for the Reserve Bank to draft, amend, and review its regulations1. Key steps before issuance of regulations and any significant amendments include public consultation and impact analysis (to the extent feasible). The framework also includes periodic review of the regulations keeping in view the stated objectives, experience gained through surveillance and supervision, relevant orders passed by courts, global best practices or standards prescribed by international standard setting bodies, relevance in a changed environment and the scope for reducing redundancies. To ensure a transparent, consultative and standardised approach in the formulation of regulations.
May 08, 2025 Investments by FPIs in Corporate Debt Securities through the General Route – Relaxations: The requirement for investments by FPIs in corporate debt securities to comply with the short-term investment limit and the concentration limit was withdrawn. To provide greater ease of investment to FPIs.
June 06, 2025 Reserve Bank of India (Lending Against Gold and Silver Collateral) Directions, 2025: As a part of moving towards a more principle-based and harmonised regulatory framework and addressing possible prudential and conduct related gaps across the REs, revised instructions on the subject were issued. To put in place a harmonised regulatory framework for loans against gold and silver collateral applicable across REs, to provide necessary clarity on applicable guidelines and strengthen the conduct-related aspects.
June 16, 2025 Master Direction – Reserve Bank of India (Electronic Trading Platforms) Directions, 2025: The regulatory framework for Electronic Trading Platforms (ETPs) issued by the Reserve Bank in 2018 were reviewed. Regulatory treatment for single dealer platforms operated by banks and standalone primary dealers were notified. Eligibility criteria to seek authorisation to operate ETPs and stipulations relating to operating framework for authorised ETPs were fine tuned. To calibrate the regulatory framework for ETPs based on changes in the market ecosystem.

2. Securities and Exchange Board of India (SEBI)

Date Regulation Rationale
October 01, 2024 Review of Stress Testing Framework for Equity Derivatives Segment for determining the Corpus of Core Settlement Guarantee Fund (Core SGF): The SEBI has specified the stress testing methodologies to be adopted for determining the credit risk of a Clearing Corporations (CCs) towards its participants. To have a more comprehensive understanding of the prevalent tail risk in the equity derivatives segment considering the changing market dynamics of the equity derivatives segment.
October 10, 2024 Change in timing for securities payout in the activity schedule for T+1 rolling settlement. To enable payout of securities to be credited to the clients’ demat account on the same settlement day instead of one working day from the receipt of pay-out from the Clearing Corporation.
November 05, 2024 Disclosure of expenses, half yearly returns, yield and ‘risk-o-meter’ of schemes of Mutual Funds: Mutual Funds were advised to disclose expenses, returns during the half year and yield of direct and regular plans of mutual fund schemes separately. Further, a standardised format and colour scheme of risk-o-meter applicable for all digital and polychrome printed promotion materials/ disclosures for the schemes have been specified. To increase transparency for all regulatory disclosures.
November 18, 2024 Modification of Para 15 of Master Circular for Credit Rating Agencies (CRAs): Specific policy guidance on the treatment of specified scenarios of non-payment of debt (principal and/ or interest) was provided. To make application of default recognition policy uniform across CRAs.
December 11, 2024 Amendment to SEBI (Issue and Listing of Non- Convertible Securities) Regulations, 2021 (SEBI NCS Regulations) regarding expanding the scope of Sustainable Finance Framework in the Indian Securities Market: The issuer will be able to raise funds through issuance of social bonds, sustainable bonds and sustainability-linked bonds which together with green debt securities will be termed as Environmental, Social and Governance (ESG) Debt Securities. To expand the scope of sustainable finance in the Indian securities market,
January 07, 2025 Measures for Ease of Doing Business for CRAs – Timelines. To facilitate ease of doing business and bring uniformity in timelines related to rating reviews and publication of Press Release by CRAs.
January 17, 2025 Disclosure of Risk Adjusted Return - Information Ratio (IR)2 for Mutual Fund Schemes: Disclosure of Information Ratio by equity schemes of Mutual Funds has been mandated, which will represent a more holistic measure of a scheme’s performance. To bring more transparency in disclosures made by AMCs and aid better decision making by investors.
January 17, 2025 Timeline for review of ESG rating pursuant to occurrence of ‘Material Events’. To enable ESG Rating Providers (ERPs) to effectively assess the impact of Business Responsibility and Sustainability Reporting (BRSR) on the ESG ratings of the rated companies.
February 27, 2025 Timelines for deployment of funds collected by Asset Management Companies (AMCs) in New Fund Offer (NFO) as per asset allocation of the scheme. To encourage AMCs to collect only as much funds in NFOs as can be deployed in a reasonable period of time and to discourage any mis-selling of NFOs of the mutual fund schemes.
March 21, 2025 Alignment of interest of the Designated Employees of the Asset Management Company (AMC) with the interest of the unitholders: Amendments to SEBI (Mutual Funds) Regulations, 1996 were carried out to relax the regulatory framework with respect to the “skin in the game requirements” applicable to AMCs and their employees. To facilitate ease of doing business for Mutual Funds.
March 28, 2025 Amendment to Master Circular for Real Estate Investment Trusts (REITs): Amendments include review of lock-in provisions for preferential issue of units for REITs and guidelines for follow-on offer by publicly offered REITs. To align the quantum of units required to be locked-in under the guidelines for preferential issue of units for REITs and Infrastructure Investment Trusts (InvITs) applicable at the time of initial offer and to provide a regulatory framework for undertaking follow-on offer by a publicly offered REIT/ InvIT.
March 28, 2025 Amendments to SEBI (Listing Obligations and Disclosure Requirements (LODR)) Regulations, 2015 regarding corporate norms for High Value Debt Listed Entities (HVDLEs): The revised framework for HVDLEs provides for the following – (a) increase in threshold for identification of HVDLE from ₹500 crore to ₹1000 crore; (b) introduction of a separate chapter and a sunset clause for HVDLEs; (c) increased flexibility on the constitution of the Nomination and Remuneration Committee (NRC), Risk Management Committee (RMC) and Stakeholder Relationship Committee (SRC) by HVDLEs; (d) inclusion of HVDLEs in computation of listed entities while counting the ceiling on the number of directorships, memberships or chairpersonships; (e) for debt listed entities where the shareholding is wholly/ substantially held by one or a few related party shareholders, material Related Party Transactions (RPTs) shall require No- Objection Certificate (NOC) from the Debenture Trustee (who, in turn, shall obtain debenture holders’ approval); (f) introduction of Business Responsibility and Sustainability Report (BRSR) for HVDLEs on a voluntary basis; and (g) relaxation to entities set up under the Public-Private Partnership mode from provisions relating to composition of directors under the SEBI LODR Regulations akin to PSUs or statutory entities. To review the corporate governance norms in the SEBI’s LODR regulations to make it relevant for debt listed entities.
April 04, 2025 Recognition and Operationalisation of Past Risk and Return Verification Agency (PaRRVA). To facilitate persons regulated by SEBI to market their risk-return performance to investors and to ensure protection of interests of investors by ensuring access of investors to verified risk-return claims.
April 22, 2025 Measures towards Ease of Doing Business (EoDB) and Investor Protection for Infrastructure Investment Trusts and Real Estate Investment Trusts: The SEBI, in consultation with various stakeholders, reviewed the extant regulatory provisions for various matters and based on the recommendations of the working group for Ease of Doing Business and Hybrid Securities Advisory Committee (HySAC), measures towards EoDB for InvITs and REITs were provided. To promote ease of doing business for activities related to REITs and InvITs.
April 22, 2025 Securities and Exchange Board of India (Real Estate Investment Trusts) (Amendment) Regulations, 2025: The amendments include the following: (a) standardising the disclosures in scheme offer document; (b) public issue process for scheme of Small and Medium Real Estate Investment Trusts (SM REITs); and (c) alignment of provisions for SM REITs vis-à-vis REITs. To promote ease of doing business for activities related to SM REITs.
April 22, 2025 Measures towards Ease of Doing Business for ESG Rating Providers (ERPs). To promote ease of doing business for ERPs following a subscriber-pays business model and to address the industry need for ESG rating of products/ issuers under the purview of other financial sector regulators/ authorities by specifying Activity Based Regulation for ERPs.
April 22, 2025 Change in cut-off timings to determine applicable Net Asset Value (NAV) with respect to repurchase/ redemption of units in overnight schemes of Mutual Funds. To operationalise the upstreaming of clients’ funds in the form of pledge of units of Mutual Fund Overnight Schemes, revised cut-off timings to determine applicable NAV with respect to repurchase of units in the overnight schemes have been prescribed.

3. Insurance Regulatory and Development Authority of India (IRDAI)

Date Regulation Rationale
November 26, 2024 A pan India Quiz organized by IRDAI to promote Insurance Awareness: In line with the vision of achieving ‘Insurance for All by 2047’ and to create more awareness on insurance products, the Insurance Regulatory and Development Authority of India (IRDAI) organised a Pan-India insurance awareness quiz – ‘BimaGyaan’, on MyGov platform. To raise awareness about the role of insurance in financial security and inclusion.
January 10, 2025 IRDAI (Regulatory Sandbox) Regulations, 2025. To promote innovation, adaptability and operational efficiency in the insurance sector, the Regulatory Sandbox framework has been further strengthened.
January 10, 2025 IRDAI (Maintenance of Information by the Regulated Entities and Sharing of Information by the Authority), Regulations 2025. The regulation mandates electronic record-keeping with robust security and privacy measures, requires regulated entities to adopt data governance framework and implement Board approved policies for record maintenance.
January 10, 2025 IRDAI (Insurance Advisory Committee) (Amendment) Regulations, 2025;

IRDAI (Re-insurance Advisory Committee) (Amendment) Regulations, 2025; and

IRDAI (Meetings) (Amendment) Regulations, 2025.
To enhance operational flexibility, governance and efficiency of conducting meetings.
January 30, 2025 Review of revision in premium rates under health insurance policies for senior citizens. To direct all general and health insurers to not to revise the premium for senior citizens by more than 10% per annum without prior consultation with the appropriate authority.
March 10, 2025 Exposure to Forward Contracts in Government Securities (Bond Forwards). To permit the insurers to undertake transactions in bond forwards as users for hedging purpose subject to certain conditions
March 13, 2025 Identification of Domestic Systemically Important Insurers (D-SIIs): The following insurers are identified as Domestic Systemically Important Insurers (D-SIIs) for FY 2024-25: (1) Life Insurance Corporation of India; (2) The New India Assurance Company Ltd.; and (3) General Insurance Corporation of India. These insurers have to raise the level of Corporate Governance, identify all relevant risks and promote a sound risk management framework and culture. Furthermore, D-SIIs are being subjected to enhanced regulatory supervision. To ensure continued functioning of D-SIIs which are critical for the uninterrupted availability of insurance services to the national economy.

4. Pension Fund Regulatory and Development Authority (PFRDA)

Date Regulation Rationale
February 24, 2025 Regarding Timely and Quality Resolution of Grievances received under Centralised Public Grievance Redress and Monitoring System (CPGRAMS) Portal. To advise intermediaries under NPS to take utmost care of grievances received at the end of intermediaries/ entities/ Government Nodal offices and ensure that they are resolved within defined turn-around time with quality resolution.
March 28, 2025 Master Circular on Investment Guidelines for UPS/NPS/ APY Schemes- Central/ State Government (default), Corporate CG, NPS Lite, Atal Pension Yojana and APY Fund Scheme: The Master Circular, among other things, increases the maximum permissible limit under equity to 25 per cent from 15 per cent and permits pension funds to invest up to 2 per cent of their Scheme AUM in equity, in stocks beyond the Top 200 and up to Top 250 of the list prepared by NPS Trust. To stipulate the guidelines for investment by Pension Funds in UPS/ NPS/ APY Schemes.
March 28, 2025 Master Circular on Investment Guidelines for NPS Tier-I & Tier-II {Other than UPS/ Central/ State Government (default), Corporate CG, NPS Lite, APY}: Pension Funds have been permitted to invest up to 2 per cent of their Equity Scheme AUM, in stocks beyond the Top 200 and up to Top 250 of the list prepared by NPS Trust. To stipulate the guidelines for investment by Pension Funds in NPS Tier-I & Tier-II.

5. Insolvency and Bankruptcy Board of India (IBBI)

Date Regulation Rationale
January 9, 2025 Circular regarding extension of time for filing Forms to monitor Liquidation and Voluntary Liquidation Processes. To ease compliance and uphold transparency in reporting requirements under the Code.
January 28, 2025 Amendment to Insolvency Professional Agencies Regulations: The amendment extends the timeline for submitting applications for the renewal of Authorisation for Assignment (AFA) from 45 days to 90 days before the expiry of the previous AFA. It also extends the timeline for the IPA to approve or reject AFA applications from 15 days to 90 days from the date of receipt. To improve operational efficiency in AFA compliance and processing.
January 28, 2025 Amendment to Liquidation Process Regulations: The amendments, inter alia, provide for the following: (a) introduce changes to Schedule I of the liquidation regulations regarding the procedure for conduct of auction of assets, such as declaration of eligibility under Section 29A, verification of eligibility of highest bidder etc.; and (b) require the liquidator to file the final report along with Form H when a scheme under Section 230 of the Companies Act, 2013, is approved by the Adjudicating Authority (AA). To enhance the efficiency of auction process and information disclosure to the Board.
January 28, 2025 Amendment to Voluntary Liquidation Process Regulations: The amendment allows the voluntary liquidation process to be completed even in the presence of uncalled capital. To facilitate smooth closure of voluntary liquidation process.
January 28, 2025 Amendment to Grievance and Complaint Handling Procedure Regulations: The amendment extends the timeline for filing grievances or complaints to 30 days from the closure of the insolvency, liquidation, or bankruptcy process by the AA, Appellate Authority, or a Court. To allow stakeholders sufficient time to raise concerns while preventing undue delays and minimizing post-closure burdens on the Insolvency Professional.
January 29, 2025 Amendment to Inspection and Investigation Regulations: The amendment introduces an explanation to the definition of “Disciplinary Committee,” clarifying that “associated” refers to involvement in the conduct of investigation or inspection, consideration of the report, or issuance of a show cause notice. To clarify the scope of involvement of whole-time members of the Board in the Disciplinary Committee in the context of matters being adjudicated by them vis-à-vis the investigations and inspections conducted by the Board.
January 29, 2025 Amendment to the Guidelines for Technical Standards for Information Utilities (IUs): The amendments, inter alia, provide for the following: (a) verification of user identity using PAN card or any other Officially Valid Document (OVD); (b) filing of information of default with the IU before filing an application under Sections 7 or 9 of the Code and issue of Record of Default thereon; and (c) expansion of terminology used for various authentication statuses for debt information within the IU along with a color-coded scheme for each term. To enhance the accuracy and reliability of default records by strengthening user identity verification, streamlining supporting document submissions and standardizing authentication status tracking within the IU.
February 3, 2025 Amendment to CIRP Regulations: The amendments, inter alia, provide for the following - (a) disclosure of corporate debtor’s MSME registration status at the Expression of Interest (EOI) stage: (b) empowering the Committee of Creditors (CoC) to invite real estate land authorities to CoC meetings, in cases involving real estate companies, without voting rights; (c) submission of a report to the CoC and AA on development rights and required permissions for real estate projects within 60 days of the insolvency commencement; (d) enabling CoC to relax certain eligibility and procedural requirements for associations or groups of allottees to submit EOI in real estate insolvency cases; (e) permitting handing over possession and facilitate registration of real estate units to allottees who have performed their obligations upon approval of 66 per cent CoC votes; (f) appointment of facilitators for a sub-class within the creditors in a class and outlining their roles and responsibilities; and (g) providing for the constitution of a monitoring committee to oversee implementation of the resolution plan, and submission of quarterly reports to the AA on the status of the same. To improve stakeholder participation, streamline real estate resolution procedures and strengthen post-approval resolution plan monitoring mechanisms.
February 11, 2025 Circular regarding intimation to the Board on the appointment of IPs under various Processes: The IBBI issued a circular requiring IPs to notify the Board of all their appointments as Interim Resolution Professional, Resolution Professional, Bankruptcy Trustee or Administrator across various processes under the Code - CIRP, liquidation, voluntary liquidation, personal guarantor to corporate debtor’s proceedings and Financial Service Providers proceedings. To streamline record-keeping and formalise the requirement for IPs to notify the IBBI of their appointments across various processes.
March 17, 2025 Circular regarding disclosure of Carry Forward Losses in the Information Memorandum (IM): IBBI issued a circular directing IPs to include a dedicated section in the IM that provides detailed information regarding the carry forward losses of the corporate debtor under the Income Tax Act, 1961. To provide potential RAs with a more comprehensive overview of the corporate debtor’s financial position, enabling them to develop informed and viable resolution plans while considering the benefits of carry forward losses.
March 28, 2025 Circular regarding Mandatory use of BAANKNET (formerly knowns as eBKray) Auction Platform for Liquidation Process. To standardise asset sales, enhance bidder participation, and improve realisation for creditors.

6. International Financial Services Centres Authority (IFSCA)

Date Regulation Rationale
February 19, 2025 IFSCA (Fund Management) Regulations, 2025: The Fund Management Regulations 2025 replace the IFSCA (Fund Management) Regulations, 2020 and key reforms include (a) lower investment thresholds; (b) extended PPM validity; (c) increased Fund Management Entity (FME) contributions; (d) simplified retail FME entry; (e) optional listing for retail schemes; and (f) global expansion simplified. To strengthen the regulatory framework for fund management within the IFSC while simplifying processes, reducing compliance costs and introducing adequate safeguards for investor protection.
February 20, 2025 Appointment and Change of Key Managerial Personnel (KMPs) by a Fund Entity: The Authority specified the manner and procedure to be followed by a FME for effecting the appointment of or change to the KMPs after the grant of registration by the Authority to the FME. To outline a clear and standardised process for the appointment and change of KMPs of the FMEs.
April 03, 2025 Circular for Revision in Reporting Formats for Fund Management Entities in IFSC. To seek salient details of retail schemes, capture granular information in certain areas for supervisory purpose, provide greater clarity to the FMEs by restructuring some of the tables, include guidance notes wherever deemed necessary and to align the formats with the recently notified IFSCA (Fund Management) Regulations, 2025.

1 For the purpose of this Framework, “Regulations” include all regulations, directions, guidelines, notifications, orders, policies, specifications, and standards as issued by the Bank in exercise of the powers conferred on it by or under the provisions of the Acts and Rules as given in its Annex.

2 IR is an established financial ratio to measure the Risk Adjusted Return (RAR) of any scheme portfolio. It is often used as a measure of a portfolio manager's level of skill and ability to generate excess returns, relative to a benchmark and attempts to identify the consistency of the performance by incorporating standard deviation/ risk factor into the calculation.

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Report of the Internal Working Group to Review the Liquidity Management Framework


Letter of Transmittal

Shri Sanjay Malhotra
Governor
Reserve Bank of India
Central Office
Mumbai – 400001

July 30, 2025

Dear Sir,

Report of the Internal Working Group to Review the Liquidity Management Framework

We are pleased to submit the report of the Internal Working Group (IWG) to Review the Liquidity Management Framework. The IWG was set up to assess the effectiveness of the existing Liquidity Management Framework with a focus on various aspects, including the continuation of Weighted Average Call Rate (WACR) as the operating target for monetary policy, efficacy of existing instruments for liquidity management, eligibility criteria for participation in the Reserve Bank’s liquidity operations, among others.

We thank you for entrusting this responsibility to the IWG and hope that the recommendations of the Group will help in the evolution of a more effective Liquidity Management Framework.

Yours faithfully,

             Sd/-
(Dr. Poonam Gupta)
      Chairperson

Sd/-
(Radha Shyam Ratho)
Member
Sd/-
(Ajay Kumar)
Member
Sd/-
(Dr. Rajiv Ranjan)
Member
     
Sd/-
(R. Lakshmi Kanth Rao)
Member
Sd/-
(Indranil Bhattacharyya)
Member
Sd/-
(G Seshsayee)
Member

Acknowledgements

The Internal Working Group places on record its deep gratitude to Shri T. Rabi Sankar, Deputy Governor, for his continuous guidance and encouragement. The Group sincerely appreciates the valuable suggestions shared by the officials of the Reserve Bank, viz., Shri Jayant Kumar Dash, former Executive Director, RBI, Shri Muneesh Kapur, former Executive Director, RBI, Ms. Dimple Bhandia, CGM, FMRD, Dr. (Smt.) Praggya Das, former Adviser-in-Charge, MPD, Shri Rakesh Tripathy, CGM, IDMD, and Shri Manoranjan Padhy, CGM, DoR. The Group is also grateful for the insightful inputs received from bankers and economists.

The Group wishes to place on record its profound appreciation for the exceptional dedication and significant contributions of the core team from the Financial Markets Operations Department (FMOD) comprising Shri T. Kiran Kumar, GM, Shri Satish Chandra Rath, GM, Shri Amarendra Acharya, Director, Shri Mohammad Shadan Khan, former AGM, Shri Sabyasachi Sarangi, AGM, Shri Rahul Mehra, AGM, Shri Shreyas K, AGM and Shri Anish Kumar Banka, AGM, in providing comprehensive research and secretarial assistance to the Group.

The Group is also thankful to Shri Dipak Chaudhari, Asst. Adviser, Shri Mukund Raman Ayyar, AGM, Shri Raumil Suthar, Manager, Shri Venkata Rohit Kumar Alluri, Manager, Ms. Vejaya Agrawaal, Manager and Ms. Twinkal Jain, Manager of FMOD for providing critical data inputs and extending their support in the editorial process.


List of Abbreviations


Executive Summary

Liquidity management is the operating procedure of monetary policy, primarily aiming to align the targeted money market rate to the policy rate, to achieve the first step in monetary policy transmission. An effective Liquidity Management Framework (LMF) facilitates the maintenance of appropriate liquidity in the banking system and fosters money market development. The Reserve Bank's LMF has undergone a process of continuous refinement over the past two decades, driven by the dynamic nature of the macroeconomic and financial environment.

The Reserve Bank’s current LMF, implemented in February 2020, has been operative for more than five years, with a brief pause during the COVID-19 pandemic. This period has provided valuable insights into the LMF's effectiveness during episodes of global shocks such as COVID-19, the war in Ukraine and the global monetary tightening cycle. Moreover, during this period, there have also been certain domestic structural changes, including increased digital payments, the operationalisation of a 24x365 payments system and adoption of “Just-in-Time” release of Centrally Sponsored Schemes funds. These developments have significantly influenced the liquidity management paradigm. In addition, capital flows have continued to exhibit large volatility with significant implications for system liquidity. In view of the factors discussed above, the Reserve Bank constituted an Internal Working Group (IWG) to evaluate the current LMF and suggest appropriate changes for effective transmission of monetary policy. The recommendations made by the Group in this report along with their rationale are summarised below.

I. WACR as the operating target

I.1 Under the existing LMF, the overnight Weighted Average Call Rate (WACR) is the target rate for liquidity operations. The share of the call money market in total overnight money market volume has dwindled over the years1, which has raised the question on its efficacy as the operating target of monetary policy. Therefore, the IWG examined this issue by considering the following.

I.2 To achieve monetary policy transmission, it is essential that entities participating in the target segment have access to central bank’s liquidity facilities, that is Liquidity Adjustment Facility (LAF)2 in case of the Reserve Bank. Further, it is vital that entities leveraging these facilities engage in lending and borrowing activities across various overnight money market segments, driven by considerations of cost and risk, thereby facilitating rate convergence across overnight money market segments. The participants in the call money market include banks and standalone primary dealers (SPDs), both of which not only have access to the Reserve Bank’s LAF but also are under the regulatory purview of the Reserve Bank. In other words, the Reserve Bank has the maximum lever over WACR as compared to any other overnight money market rate.

I.3 The Group noted that using the uncollateralised rate as the operating target also has other advantages like it directly influences short-term interest rates and clearly conveys the central bank's stance on liquidity and interest rates. The uncollateralised rate better reflects credit/counterparty risk that is not masked by collateral. Central banks being the sole supplier of reserves have more control over this rate. A stable and predictable uncollateralised rate facilitates a more effective transmission of monetary policy, as it anchors other interest rates across maturities.

I.4 Further, it is observed that WACR and rates in the overnight collateralised segments (Triparty Repo and Market Repo) display a high degree of correlation among them and are in close alignment across time horizons. WACR is also found to be effective in transmitting signals to other market rates like treasury bills (T-bills), commercial papers (CPs), certificate of deposits (CDs) and government securities (G-secs) etc. It is equally pertinent to note that while the collateralised market segment accounts for significant portion of the total overnight money market volume, it features a notable presence of non-bank entities such as mutual funds, insurance companies and pension funds, whose transactions in the overnight money market segments do not reflect the dynamics of inter-bank market for reserves and these entities are not regulated by the Reserve Bank.

I.5 Considering the aforementioned factors, the Group recommends continuation of overnight Weighted Average Call Rate as the operating target. The Reserve Bank may, however, continue to keep track of rates in other overnight segments to ensure orderly evolution of money market rates and smoothen transmission.

II. Corridor System

II.1 The LAF under the current LMF is based on the corridor system, with the policy repo rate in the middle of the corridor, the Marginal Standing Facility (MSF) rate (25 basis points above the policy repo rate) as the ceiling and the Standing Deposit Facility (SDF) rate (25 basis points below the policy repo rate) as the floor. While widening the corridor can encourage relatively higher inter-bank activity, it may lead to increased day-to-day volatility in the short-term market rates. On the other hand, a narrow corridor may provide the advantage of better anchoring of short-term market rates but comes at the cost of reduced incentives for banks to indulge in inter-bank trading.

II.2 In view of the above considerations, the Group recommends continuation of the existing corridor system with policy repo rate at the middle of the corridor. The corridor would remain symmetric, with SDF rate and MSF rate, which are 25 basis points away from the policy repo rate, acting as the lower and upper bounds of the corridor, respectively.

III. Operational Toolkit of the Liquidity Management Framework

III.1 Under the existing LMF, the 14-day Variable Rate Repo/Reverse Repo (VRR/VRRR) auction is the main operation to manage transient/short-term liquidity needs, supported by fine-tuning operations of overnight to 13 days tenors. An empirical analysis shows that while repo operations across tenors were generally over-subscribed, there was a general reluctance of banks to part with their surplus liquidity in 14-day main reverse repo operations, preferring instead, utilisation of the SDF facility on a daily basis. While fine-tuning VRRR auctions saw relatively higher participation, the increased number of fine-tuning operations, in general, leads to uncertainty about the Reserve Bank’s liquidity operations. The major reason for lower participation of banks in VRRR main operations is the limited information that banks have on the frictional factors like changes in Government of India (GoI) cash balances, posing challenges in liquidity forecast for a longer tenor.

III.2 The challenges faced by banks in forecasting the liquidity position for a 14-day period and resultant lower participation in 14-day main operations, undermines the efficacy of 14-day operations in managing transient liquidity. The Group is of the view that intra-fortnight variations in the system liquidity due to frictional factors like changes in government balances can be better managed through shorter tenor operations instead of 14-day operations. Therefore, the Group recommends that 14-day VRR/VRRR auctions may be discontinued as the main operation. Instead, the transient liquidity may be managed primarily through 7-day repo/ reverse repo operations and other operations of tenors from overnight up to 14-days at the discretion of the Reserve Bank based on its assessment of the system liquidity requirement.

III.3 Further, to reduce uncertainty in the market about the tenor, quantum and timing of the repo/reverse repo operations, the Group felt that it is desirable for the Reserve Bank to provide sufficient advance notice to market participants, at least by one day, while conducting any such liquidity operation.

III.4 The Group observed that there may be situations in which the evolving liquidity conditions may warrant conduct of liquidity operations on the same day of announcement. The Group recommends that in such circumstances, the Reserve Bank may conduct repo/reverse repo operations announced on the same day.

III.5 As regards the mechanism for repo and reverse repo operations, the Group noted that the variable rate auctions provide the advantage of better price signaling and consequent efficient allocation of reserves among the market participants. It is viewed that, for liquidity management purpose, variable rate liquidity operations are more appropriate tools than fixed rate operations as the former contains useful market signals about liquidity conditions. In view of the above, the Group recommends continuation of variable rate auction mechanism for conducting repo and reverse repo operations under the LAF.

III.6 The repo/reverse repo operations of tenor overnight to 14-days are expected to handle the changes in system liquidity due to frictional factors. However, in some scenarios, the liquidity mismatches may extend for a longer term. For handling such liquidity mismatches, the Group recommends that the repo/reverse repo operations of appropriately longer tenor may be conducted through variable rate auctions. The availability of such instruments would provide the necessary liquidity assurance to the market and would also help in minimising frequency and quantum of shorter tenor operations.

III.7 The Group noted that the set of instruments in the extant LMF, viz., Open Market Operations (OMOs), long-term VRR/VRRR operations and Foreign Exchange (FX) swap auctions, are sufficient for managing durable liquidity in the system and hence does not recommend any change at this stage.

IV. Standalone Primary Dealers

Standalone Primary Dealers (SPDs) are heavily dependent on overnight segment of the money market to meet their funding requirements. They have been the largest borrowers in the call money market contributing about three-fourth of the total borrowing. The Group noted that SPDs have already been allowed to participate in all repo operations irrespective of tenor with effect from March 26, 2025. The Group also felt that SPDs may not be given access to MSF, as hitherto, as unlike banks they have neither reserve requirements nor unforeseen payment obligations beyond market hours.

V. Averaging of Reserve Requirement

Averaging of reserve requirement over the maintenance period allow banks to use excess reserves from one day to offset shortfall on other days. In the context of 24x365 payment systems, which has resulted in banks building up precautionary reserve balances, there is a view that some reduction in the 90 per cent daily maintenance requirement is warranted as it may provide further room for banks to effectively manage their liquidity over the maintenance period. However, lowering daily Cash Reserve Ratio (CRR) maintenance requirement may induce higher volatility in money market rates, especially during end of fortnights, which may not be desirable. It has also been observed that banks rarely maintain daily reserve balances below 95 per cent of the prescribed CRR. In view of the above, the Group recommends the Reserve Bank to retain the extant daily minimum requirement of 90 per cent of the prescribed CRR.


I. Introduction

I.1. Liquidity management is the operating procedure of monetary policy.3 Specifically, liquidity management operations are intended to transmit the impulse of monetary policy action to the call money market, which is the market for bank reserves (deposits placed by banks with the central bank). Since the successful conduct of monetary policy requires effective liquidity management operations, the Liquidity Management Framework (LMF) needs to be carefully designed and deployed.

I.2. The current LMF of the Reserve Bank was implemented in February 2020 and has been in operation for more than five years, with a brief suspension in April 2020 due to COVID-19-induced disruptions. Normal liquidity management was restored in a phased manner starting from January 2021. In these five years, the Reserve Bank has gained valuable insights from operations under the LMF during episodes of global shocks such as the COVID-19, the war in Ukraine and the global monetary tightening cycle., including policy rate hikes in India.

I.3. During this period, there have also been certain structural changes which have influenced the liquidity management paradigm of market participants in a significant manner. Functioning of a 24x365 payments system4 requires banks to be in readiness for settlement of large payment obligations. Moreover, capital flows have continued to exhibit large volatility with significant implications for the system liquidity. The flows arising on account of GoI transactions impact the banking system liquidity on a daily basis. The adoption of “Just-in-Time” release of funds under Centrally Sponsored Schemes in July 20235 has added another dimension to liquidity management by banks.

I.4. In view of factors discussed above, and to give effect to insights gained from implementation of the current LMF, it was considered appropriate and timely to review the existing framework. Accordingly, the Reserve Bank constituted an Internal Working Group (IWG) (hereinafter ‘the Group’) to evaluate the current LMF and suggest changes appropriate to the evolving financial and technological environment for effective transmission of monetary policy. The composition of the IWG is as under:

  • Dr. Poonam Gupta, Deputy Governor (Chairperson)
  • Shri Radha Shyam Ratho, Executive Director
  • Shri Ajay Kumar, Executive Director
  • Dr. Rajiv Ranjan, Executive Director
  • Shri R. Lakshmi Kanth Rao, Executive Director
  • Shri Indranil Bhattacharyya, Executive Director
  • Shri G. Seshsayee, Chief General Manager (Member Secretary).

Terms of Reference of the IWG

i. Assess the effectiveness of the existing Liquidity Management Framework (LMF) in meeting its objectives and suggest changes

ii. The Group may, inter-alia, critically examine,

  1. continuation or otherwise of Weighted Average Call Rate (WACR) as the operating target for the monetary policy and suggest alternative, if any;
  2. efficacy of existing liquidity management instruments and suggest changes;
  3. the eligibility criteria for participation in the LAF operations including that of Standalone Primary Dealers (SPDs);
  4. any other improvements in the liquidity management framework.

II. Evolution of Liquidity Management Framework

Over the past two decades, the Reserve Bank’s LMF has continuously evolved in response to the changing macroeconomic and financial environment.

II.1. The LMF of the Reserve Bank has evolved through progressive refinements since 1999 in response to changing domestic conditions and global developments. In April 1999, an Interim LAF was introduced under which liquidity was injected against collateral of GoI securities at various interest rates, but surplus liquidity was absorbed at a fixed rate. The transition from the Interim LAF to a full-fledged LAF took place in June 2000.

II.2. A new operating framework for monetary policy was implemented in May 2011 to enhance the effectiveness and stability of the monetary system. The framework recommended repo rate as the single policy rate and suggested overnight WACR as the operating target of monetary policy. To manage market liquidity and interest rates effectively, an interest rate corridor of (+/-) 100 basis points (bps) around the repo rate was introduced, with the MSF rate set at 100 bps above the repo rate and the reverse repo rate set at 100 bps below it. The framework aimed to fully accommodate liquidity demand at fixed repo rate, maintaining an indicative comfort zone of (+/-) 1 per cent of the Net Demand and Time Liabilities (NDTL) of the banking system. Additionally, the framework facilitated transmission of changes in repo rate through WACR to the term structure of interest rates, thereby influencing broader financial conditions and economic activities.

II.3. The LMF was revamped in September 2014 offering banks assured liquidity access equivalent to 1 per cent of their NDTL on an average. It included provisions for overnight fixed-rate repos up to 0.25 per cent of NDTL, while remaining liquidity needs were met through conduct of four 14-day variable rate term repo auctions during the reporting fortnight. Additionally, the introduction of variable rate fine-tuning repo and reverse repo auctions allowed the Reserve Bank to manage liquidity conditions more precisely and respond dynamically to market needs.

II.4. Further refinements in the LMF were carried out in April 2016 and April 2017. The changes included progressive narrowing of the interest rate corridor around the policy repo rate from (+/) 100 bps to (+/-) 50 bps and further to (+/-) 25 bps to provide more control over short-term interest rates and reduce money market volatility. The framework also aimed to progressively lower the ex-ante system-level liquidity deficit, bringing it closer to a neutral position over the medium term. Additionally, the minimum daily maintenance requirement of the CRR was reduced from 95 per cent to 90 per cent with effect from the fortnight beginning April 16, 2016, granting banks greater flexibility in managing their daily liquidity needs.

II.5. The Reserve Bank announced a revised LMF in February 2020. The framework retained the liquidity management corridor with WACR as the operating target and retained the corridor width at 50 bps. The main tool for managing transient liquidity was a 14-day term repo/reverse repo operation, aligned with the CRR maintenance cycle, while daily fixed rate repos and fortnightly term repos were withdrawn. Fine-tuning operations supported the main liquidity operation to address any unanticipated liquidity changes. Instruments included fixed rate reverse repo, MSF, variable rate repo/reverse repo auctions, OMOs and FX swaps. The minimum daily CRR maintenance requirement remained at 90 per cent. SPDs were allowed to participate only in overnight liquidity management operations. They were, however, not granted access to the MSF. The framework emphasized transparency through dissemination of flow and stock impact of liquidity operations and publication of a quantitative assessment of durable liquidity conditions with a fortnightly lag.

II.6. SDF, which was operationalised with effect from April 8, 2022, replaced the erstwhile Fixed Rate Reverse Repo (FRRR) as the floor of the LAF corridor. Further, SPDs were allowed to participate in all repo operations irrespective of tenor with effect from March 26, 20256.


III. Guiding Principles of Liquidity Management Operations

III.1. Liquidity management, as stated in the introduction, is the operating procedure of monetary policy. Its primary objective is to ensure that the targeted money market rate remains aligned to the policy rate. Liquidity management thus focuses on the first step in monetary policy transmission. Liquidity operations are aimed at aligning the target rate to the policy rate. The objective of this transmission of policy impulse to overnight market rates is that further transmission to other interest rates in the economy (term money market rates, G-sec yields, corporate bond yields, bank deposit and lending rates etc.) happens efficiently. If the transmission to overnight rates is incomplete, then the transmission to other market interest rates would also be less than optimal.

III.2. In a growing economy like India, while the need for reserves by the banking system tends to be on an upward trajectory owing to the secular growth in NDTL, challenges to the liquidity management stem from certain structural factors discussed earlier such as 24x365 payments, increased volatility in government balances considering the introduction of “Just-in-Time” release of funds, the Reserve Bank’s forex operations, etc. Given the nature of these factors, there is a significant degree of uncertainty about their impact on the system liquidity.

III.3. It is desirable that banking system liquidity should not remain in excessive deficit or surplus for extended period. Excessive surpluses or deficits give rise to imbalances such as banks holding on to their funds rather than lending in the market or can generate structural constraints like shortage of collateral in the system. If that happens because of unavoidable reasons, durable liquidity operations should be resorted to, and the liquidity position be brought back to manageable levels. For instance, during COVID-19 pandemic, in an attempt to ensure normal functioning of the financial markets, various liquidity measures were adopted by the Reserve Bank to infuse long-term liquidity into the system. These extraordinary liquidity measures resulted in prolonged period of surplus system liquidity7, reaching up to 6 per cent of NDTL of the banking system8. Subsequently, surplus liquidity infused into the system was gradually withdrawn over time while ensuring there was no major impact on financial markets.

III.4. The effectiveness of the LMF is premised on existence of an active overnight money market. The LMF should incentivise banks to trade among themselves rather than with the central bank because the transmission process crucially depends on market forces working efficiently. That is, it should address shortage or excess liquidity at the system level, not that of individual entities, as the latter would effectively mean that the Reserve Bank is acting as a market maker. The LMF should also facilitate the development of money markets so as to achieve efficient transmission of monetary policy.

III.5. Selecting an appropriate design of the LMF and defining an appropriate target rate are crucial for monetary policy operations to achieve its objective.

III.5.1. Broadly two types of LMFs are prevalent globally, viz., corridor system and floor system. In a corridor system, the central bank maintains standing facilities on either side of the corridor viz., a deposit facility wherein participants can deposit their excess reserves at the end of the day and a lending facility wherefrom participants can borrow reserves in times of need. Therefore, the deposit rate and the lending rate under the standing facilities effectively become the floor and the ceiling of the corridor respectively, with the policy rate in between. The central bank modulates the quantum of available liquidity in the system to keep the overnight rates within the corridor and aligned to the policy rate. On the other hand, in a floor system, the central bank is required to maintain only a deposit facility to act as the floor and the policy rate is defined at the floor rate. By supplying abundant liquidity, the central bank can maintain the overnight rates at the deposit rate thereby aligning the market rates to the policy rate.

III.5.2. With regard to the operating target, the choice should be based on “first principles” of liquidity management rather than being determined by the phase of the liquidity cycle (easy vis-à-vis tight) or convenience of market participants. As the central bank supplies reserves through the LMF, the target rate should preferably be from a market that trades in reserves. The target rate should also have a strong correlation to the other segments of the money market and one that influences the benchmark rates. Further, all the participants in the target money market should have access to the central bank’s liquidity facilities (LAF in the case of the Reserve Bank) so that the effect of monetary policy actions can be transmitted seamlessly.

III.6. In the Indian context, the Reserve Bank’s LMF is currently a corridor system with WACR as the target rate. The mechanics of the LMF that ensure that the target rate is aligned to the policy repo rate in the Indian context is explained below.

III.7. The first step in achieving this objective is to define upper and lower limits within which the target rate is to be kept. Liquidity operations then need to ensure that the target rate remains within the policy corridor. At the lower end of the corridor is the floor rate – the rate at which the Reserve Bank absorbs surplus funds from the banking system, currently the SDF rate which replaced the FRRR with effect from April 8, 2022. At the upper end of the corridor is the ceiling rate - the rate at which the Reserve Bank assures provision of funds to the banking system, currently the MSF rate.

III.8. How is the target rate, which is a market determined rate, contained within the corridor? This is achieved by assuring the banking system that, (a) if the system is in liquidity deficit, the Reserve Bank shall meet the deficit up to a certain limit, at the ceiling rate9 and (b) if the system is in liquidity surplus, the Reserve Bank shall absorb the surplus at the floor rate. This assurance ensures that no market participant would lend in the money market below the floor rate or borrow in the money market above the ceiling rate. Subject to participants not being constrained by their ability to tap MSF, the policy corridor serves the purpose of constraining volatility in the targeted money market segment rate within the width of the corridor, currently 50 bps.

III.9. Having ensured that the target rate is contained within the policy corridor, the next step is to align it to the policy rate. A simple method is to provide market participants incentive to transact amongst each other, while conducting appropriate liquidity operations to maintain adequate liquidity at the systemic level. As long as there is an assurance from the Reserve Bank of provision of liquidity as per the systemic needs, there is very little incentive for any bank in the system to borrow at significantly higher rates in the market. In other words, the target rate will not deviate significantly from the policy repo rate. Thus, the objective of LMF will be achieved.

III.10. Certainty of liquidity management operations in a broad sense and provision of required liquidity to the banking system by the Reserve Bank are the key principles of the LMF. Clear communication aimed at increasing certainty about liquidity conditions and providing assurance to market participants, should be an underlying factor in the design of the LMF. Further, minimizing the number of operations to bring in more clarity about the conduct of liquidity operations would also be an important factor in improving the efficiency of monetary policy transmission.


IV. An Overview of the Money Market

IV.1. The money market is a key component of the financial system as it is the fulcrum of monetary operations conducted by the central bank in its pursuit of monetary policy objectives. It is a market for short-term funds with maturity ranging from overnight to one year and includes financial instruments that are deemed to be close substitutes of money.

IV.2. The overnight money market in India is the most liquid money market segment with an average daily volume of over ₹5 lakh crore. It comprises the uncollateralized (call) and collateralized (repo) segments. In terms of overall market comparison, the triparty repo segment constitutes the largest share with around 70 per cent of the money market volumes while the market repo and the call money market segments constitute around 28 per cent and 2 per cent of the money market volumes, respectively (Chart IV.1).

Chart IV.1: Composition of Overnight Money Market Volumes

Call Money Market

IV.3. The call money market is an uncollateralised market for bank reserves. In addition, SPDs are also permitted to participate in this market. The participant profile in the call money market indicates that SPDs are the major borrowers, while co-operative banks are the major lenders (Chart IV.2.a and Chart IV.2.b).

Chart IV.2: Participants’ Profile in Call Money Market#

IV.3.1. Monthly Volume: During the period January 2023 to March 2025, the average daily trading volumes in the call money market have ranged from ₹9,000 crore to ₹15,000 crore (Chart IV.3).

Chart IV.3: Average Trading Volume in Call Money Market

IV.3.2. Trading venue and settlement: The primary trading venue for call money market transactions is the Negotiated Dealing System-Call (NDS-Call) platform. All call money market transactions are settled bilaterally. With increasing participation of co-operative banks as lenders in the call money market, a larger share of trades was taking place outside the NDS-Call. Prior to 2021, these trades were also taking place at relatively lower rates, thus impacting the WACR. To address this and to improve transparency in the market, all call money market participants were mandated to obtain membership of the NDS-Call platform in 2021. This has resulted in gradual reduction and elimination of reported trades and currently all market participants are undertaking call trades on the NDS-call platform leading to better alignment of rates in the call money market (Chart IV.4).

Chart IV.4: Volume of NDS - Call and reported segment in Call Money Market

Triparty Repo in Government Securities

IV.4. The triparty repo segment is a collateralised segment and the largest segment of the money market with major participation by non-bank lenders such as mutual funds, insurance companies and provident funds. The participant profile in the triparty repo market indicates that public sector banks are largest borrowers followed by private sector banks, while mutual funds are major lenders (Chart IV.5.a and IV.5.b).

Chart IV.5: Participants’ Profile in Triparty Repo#

IV.4.1. Monthly Volume: The average daily volume in the triparty repo segment has been around ₹3.5 - ₹4 lakh crore during the period January 2023 to March 2025 (Chart IV.6).

Chart IV.6: Average Trading Volume in Triparty Repo

IV.4.2. Trading venue and settlement: The trading venue for triparty repo transactions is Triparty Repo Dealing System (TREPS), an anonymous order matching platform. All triparty repo transactions are settled through the Clearing Corporation of India Ltd. (CCIL).

Market Repo

IV.5. The market repo segment is also a collateralised segment. Transactions in market repo are undertaken predominantly on Clearcorp Repo Order Matching System (CROMS) which also facilitates trading in special repos. Similar to the triparty repo segment, the market repo segment has participation by major non-bank lenders such as mutual funds, insurance companies, provident funds. etc., while SPDs and foreign banks are the largest borrowers (Chart IV.7.a and IV.7.b).

Chart IV.7: Participants’ Profile in Market Repo#

IV.5.1. Monthly Volume: The average daily volume during the period January 2023 to March 2025, shows a uniform trend of daily average of around ₹1.5 lakh crore (Chart IV.8).

Chart IV.8: Average Trading Volume in Market Repo

IV.5.2. Trading venue and settlement: The primary trading venue for market repo transactions is CROMS, an anonymous order matching platform. All market repo transactions are settled through CCIL.


V. Liquidity Management Framework – Issues and Recommendations

V.1. Drawing insights from the experience gained during the tenure of the current LMF and developments in the money market, this chapter presents an analytical review of various issues of the current LMF and suggests recommendations wherever required.

The Operating Target

V.2. Under the existing LMF, the WACR is the operating target of monetary policy. The objective of liquidity management operations is to align the target rate to the policy repo rate. Pursuant to the recommendation of the “Working group on Operating Procedure of Monetary Policy” (Chairman: Deepak Mohanty, RBI, 2011), the WACR was recommended to be the operating target of monetary policy of the Reserve Bank. Since then, the LMF has undergone major changes in 2014 and 2020, however, WACR has continued to remain the operating target. Since 2011, volumes in call money market, which is uncollateralised, have reduced from 16 per cent to about 2 per cent of total overnight money market volumes during 2022-2025 (Chart V.1). This change indicates a shift in liquidity management preference of market participants towards collateralised borrowing/lending aided by regulatory policies. The participation base in call money market has also changed with the share of scheduled commercial banks reducing over time accompanied by a significant increase in the share of SPDs.

V.3. Domestically, this shift in the trends has been influenced by increasing participation of non-bank lenders (especially mutual funds) in secured overnight money markets. Regulatory changes have also left banks with a larger holding of excess Statutory Liquidity Ratio (SLR) securities which can be used to meet their funding needs in collateralised market, with lower associated capital and other regulatory costs. It may, however, be noted that shift in money market volumes towards collateralised segments is a universal phenomenon during the post-Global Financial Crisis era. Despite this, most central banks continue to have unsecured inter-bank rate as their operating target.10

Chart V.1: Trends in Composition of Overnight Money Market

V.4. For the central bank’s liquidity management operations to be effective, there are broadly two considerations regarding the operating target:

  1. entities that are active in the target money market segment, must also have access to the central bank’s liquidity facilities (LAF in the case of the Reserve Bank).
  2. entities that access these facilities, do onward lending or borrowing driven by cost and risk considerations for rates to equilibrate among different overnight money market segments.

V.5. The consideration at V.4.(a) mentioned above is imperative to ensure that rates in the overnight money market segment reflect, to the extent possible, the impact of central bank’s operations. The operating target should be the one which the monetary authority can effectively control, allowing it to achieve its broader objectives. The participants in the call money market in India include banks and SPDs, both of which are regulated by the Reserve Bank and have access to the LAF11. Therefore, the Reserve Bank has the maximum lever over WACR as compared to any other overnight money market rate.

V.6. Further, the rationale for using the uncollateralised rate as the operating target also stems from its ability to directly influence short-term interest rates and provide a clear signal of the central bank's stance on liquidity and interest rates. This approach has several advantages, viz., (i) the uncollateralised rate provides a better assessment of credit/counterparty risk that is not masked by collateral; (ii) central banks have more control over the uncollateralised overnight rate as they are the sole supplier of bank reserves; (iii) targeting the uncollateralised rate provides a clear signal to financial markets about the central banks’ desired level of interest rates; (iv) a stable and predictable uncollateralised rate facilitates a more effective transmission of monetary policy since all other rates across the term structure are priced off the uncollateralised rate; and (v) communicating policy changes through the uncollateralised rate are less prone to misinterpretations. Overall, the uncollateralised rate serves as a readily observable indicator of the effectiveness of monetary policy and provides a mechanism for the central bank to guide market participants' expectations about future interest rate movements.

WACR and Other Money Market Rates – An Analysis

V.7. It is observed that WACR and rates in collateralised segments (Triparty repo and Market repo) broadly move in tandem, thus emphasising consideration at V.4.(b) as mentioned above. Generally, WACR has remained above triparty repo and market repo rates with average daily spreads at 7 bps and 4 bps, respectively, during the period April 2023 to July 2025 (Table V.1 & Chart V.2). However, these spreads have shown some variability over time with a standard deviation of 8 bps each.

Table V.1: Summary Statistics of Overnight Rates and Spreads
(April 1, 2023 to July 10, 2025)
  WACR Triparty Repo
(WAR)
Market Repo
(WAR)
Spread of WACR over
Triparty Repo
(WAR)
Market Repo
(WAR)
Mean 6.46% 6.39% 6.42% 7 bps 4 bps
Standard Deviation 0.36 pp 0.38 pp 0.39 pp 8 bps 8 bps
Source: RBI Staff Calculations.
Note: pp denotes percentage points

Chart V.2: Daily Overnight Money Market Rates

V.8. An analysis with a longer horizon (from 2011) shows that the triparty repo rate is broadly aligned with the WACR with an equally high correlation (Chart V.3).

Chart V.3: Overnight Rates (Monthly Average)

V.9. The high correlation between WACR and collateralised rates (Chart V.2 and Chart V.3) suggests that signals from the former are effectively transmitted to secured overnight rates. WACR is also found to be effective in transmitting signals to other market rates like Treasury bills (T-Bills), CPs, CDs and G-secs as suggested by contemporaneous correlations between these rates (Table V.2).

Table V.2: Correlation Matrix (April 1, 2023 to July 10, 2025)
  Repo Rate 91D T-bill 3M CD 3M CP 3Y G-sec
WACR 0.917 0.901 0.793 0.771 0.854
Triparty Repo (WAR) 0.874 0.889 0.745 0.729 0.859
Source: RBI Staff Calculations.
 

V.10. Considering the significant increase in market share of collateralised overnight money market segments, there is a view that collateralised money market rates may also be considered for the operating target. However, as these segments feature a notable presence of non-bank entities such as mutual funds, insurance companies and pension funds, transactions in these segments may not reflect the dynamics of inter-bank market for reserves and also these entities are not regulated by the Reserve Bank. Further, based on consideration V.4.(a) mentioned above, it may necessitate provision of access of Reserve Bank’s liquidity facilities under the LAF to non-bank entities like mutual funds, insurance companies, provident funds, All India Financial Institutions (AIFIs) etc. As this measure could have far reaching implications for the financial system as a whole, it requires a comprehensive examination separately.

V.11. Further, it is also observed that the variability in triparty repo and market repo is generally higher than in WACR (Table 1). Larger variability may lead to less accurate signals and may also necessitate increased frequency and volume of Reserve Bank’s liquidity operations.

V.12. In view of the above, the Group recommends that the overnight Weighted Average Call Rate may continue as the operating target of monetary policy. The call money market reflects transaction dynamics of market for reserves and is the only market segment containing the Reserve Bank’s regulated entities only, with most of them having access to the LAF. The Reserve Bank may, however, continue to keep track of rates in other overnight segments to ensure orderly evolution of money market rates and smoothen transmission. Accordingly, the Reserve Bank’s liquidity management operations may take into account movement in secured rates to ensure better alignment of overnight rates with the policy repo rate. If market participants efficiently exploit arbitrage opportunities, interest rates across different markets will align automatically.

Corridor System

V.13. The LAF under the current LMF is based on the corridor system, with the policy repo rate in the middle of the corridor. The MSF rate acts as the ceiling (25 bps above the policy repo rate) while the SDF rate acts as the floor (25 bps below the policy repo rate) of the corridor.

V.14. Reducing the volatility in the inter-bank money market rate while achieving the interest rate target is both an objective and a challenge for efficient liquidity management. Widening the corridor can encourage relatively higher inter-bank activity as the opportunity cost of holding excess reserves or being short of required reserves is higher, thereby nudging banks to manage their liquidity pro-actively12. However, a wider corridor may lead to increased day-to-day volatility in the short-term market rates, which may also impart volatility to the longer end of the term structure of interest rates. On the other hand, a narrow corridor may provide the advantage of better anchoring of short-term market rates but comes at the cost of reduced incentives for banks to indulge in inter-bank trading.

V.15. A comparison of LMFs of countries operating under the corridor system suggests that corridors are typically wider in most emerging markets while the advanced countries tend to have narrow corridors (Annex). As emerging countries are often more susceptible to large capital flow shocks, a wider corridor system is more suitable for them as it offers the desired flexibility to operate the LMF both in conditions of liquidity deficit and surplus. However, a wider corridor entails more volatility in money-market rates.

V.16. Among advanced economies, the European Central Bank (ECB), the Bank of Canada (BoC), the Reserve Bank of New Zealand (RBNZ), and the Reserve Bank of Australia (RBA) were the pioneers of the symmetric corridor system around the year 2000. The Norges Bank implemented monetary policy through a relatively pure version of a floor system until October 2011; thereafter, it shifted to a quota-based system – a compromise between a floor system and a corridor system. Central banks like the BoC and the RBNZ adopted the floor system in 2020.

V.17. In the recent period, the Bank of England (BoE), the BoC, the ECB, and the RBA have all announced plans to reduce reserves until borrowing from the central bank picks up and market rates are marginally above the interest rate paid by the central bank on deposits, essentially returning to a corridor system, although they do not refer to it as the “corridor” (Nelson, 2024). The new ‘soft’ floor framework with a narrower spread can be characterised as a hybrid system, combining the smallest possible central bank balance sheet with both structural and fine-tuning operations. Its main objective is to allow for effective control of short-term money market rates in transition from a situation of abundant excess liquidity to one of less ample liquidity (Höflmayr and Kläffling, 2024).

V.18. The global shift towards reducing corridor width reflects central banks' efforts to refine liquidity management and enhance monetary policy transmission. The ECB announced a new operational framework, effective September 2024, aimed at transitioning from excess to less ample liquidity. Under this framework, the main refinancing operations (MRO) rate was positioned 15 bps above the floor i.e., deposit facility rate (DFR), instead of 50 bps prior to this new framework. The marginal lending facility (MLF) rate which acts as the ceiling continued to remain 25 bps above the MRO rate. This adjustment effectively narrowed the corridor width from 75 bps to 40 bps, helping to anchor market rates closer to the DFR and mitigate liquidity-driven volatility.

V.19. Similarly, the RBA announced in April 2024 its shift from an excess reserves system to an ample reserves framework. According to the RBA, the move towards ample reserves, which lies somewhere between the prevailing ‘floor’ system with excess reserves and the pre-pandemic ‘corridor’ system with scarce reserves, eliminates the need for precise reserve estimations and reduces money market fluctuations.

V.20. In the Indian context, the LAF interest rate corridor was symmetric with a width of 200 bps at its inception in May 2011 and continued to be so till mid-July 2013. Consequent to the volatility wrought by the taper tantrum on domestic financial markets, the Reserve Bank widened the corridor asymmetrically to 400 bps in mid-July 2013 (with the ceiling 300 bps above the policy rate and the floor 100 bps below the policy rate). With the return of normalcy, the corridor width was gradually restored to its pre-crisis level by end-October 2013. Subsequently, the corridor was narrowed from 200 bps to 100 bps in April 2016 and further to 50 bps in April 2017, while maintaining its symmetricity. Following the financial market dislocations consequent to the outbreak of the covid pandemic, the corridor was asymmetrically widened to 90 bps in two stages with the FRRR rate 65 bps below and the MSF rate 25 bps above the policy repo rate, respectively. The width was restored to 50 bps after the introduction of the SDF at 25 bps below the policy repo rate on April 8, 2022, which reinstated the symmetricity of the corridor.

V.21. Corroborating the experience of the euro area, volatility of the inter-bank overnight uncollateralised call rate – as measured by the exponential weighted moving average (EWMA)13 of the WACR – moderated with the progressive narrowing of the corridor (Chart V.4).14 The narrowing of the corridor, however, was coincidental with the declining share of call money in the total overnight money market volume (Chart V.5).

Chart V.4: Corridor Width and WACR Volatility
Chart V.5: Corridor Width and Call Market Share

V.22. The Group, therefore, recommends continuation of the existing corridor system with policy repo rate at the middle of the corridor. The corridor would remain symmetric, with SDF and MSF, which are 25 bps on either side of the policy repo rate, acting as the lower and upper bounds of the corridor system, respectively.

Skewness in Liquidity Distribution

V.23. In last few years, it is observed that even on days when system liquidity remained in deficit, some banks parked significant amount of funds with the Reserve Bank under the SDF (Chart V.6). While this is reflective of skewness in the distribution of liquidity in the system and reluctance on the part of banks in trading among themselves, it is also an indication that the Reserve Bank might actually be functioning as a market maker to banks. While some banks were found to be borrowing through repo auctions and/or MSF window, some other banks were parking their surplus liquidity under the SDF on the same day.

Chart V.6: Average SDF/FRRR vs System Liquidity

V.24. Another important factor contributing towards higher amount under the SDF is the fact that banks tend to maintain precautionary balances to meet any unforeseen payment obligations arising beyond market hours due to availability of payment systems on a 24x365 basis. Any excess amount after meeting payment obligations is then parked under the SDF. This behaviour was exacerbated because there was no funding avenue for banks as the money market used to close at 5:00 PM. The Group noted that the extension of timings of the call money market up to 7:00 pm with effect from July 1, 2025, based on the recommendation of “Report of the Working Group on Comprehensive Review of Trading and Settlement Timings of Markets Regulated by the Reserve Bank” (Chairman: Shri Radha Shyam Ratho, RBI, 2025), would help in reducing banks’ precautionary demand for reserves to meet any unforeseen payment obligations.

Operational Toolkit of the Liquidity Management Framework

V.25. In the extant LMF, the 14-day Variable Rate Repo/Reverse Repo (VRR/VRRR) auction, which coincides with CRR maintenance cycle, is the main liquidity operation to handle transient liquidity. Additionally, fine-tuning operations which include VRR/VRRR operations of tenors overnight to 13 days, conducted at the discretion of the Reserve Bank, provide the flexibility to handle liquidity mismatches that may occur during the 14-day period.

V.26. An analysis of the bid-to-cover ratios of variable rate liquidity operations conducted over the past two years shows a stark difference in participation of banks in the VRR and VRRR main operations (Chart V.7). During the calendar year 2024, the Reserve Bank conducted 25 main operations (14 VRR and 11 VRRR operations) and 147 fine-tuning operations (56 VRR and 91 VRRR). While VRR auctions (both main and fine-tuning) were generally over-subscribed, indicating preference of banks for any additional liquidity from the Reserve Bank, the VRRR auctions generally saw lower participation from banks.

V.27. Further analysis of the bid-to-cover ratios of VRRR operations shows that VRRR main operations saw muted response from banks with the average bid-to-cover ratio of only 0.18 during the calendar year 2024. On the other hand, comparatively higher average bid-to-cover ratios in VRRR fine-tuning operations and large SDF balances were observed over the period of VRRR main operations (Chart V.7 and Chart V.8). This shows a clear preference of banks to deposit their excess reserves with the Reserve Bank for overnight/shorter tenors rather than participating actively in the 14-day main operation, despite holding excess reserves over the period of the main operation, thus foregoing a spread of up to 24 bps in the process15.

V.28. The major reason for lower participation of banks in VRRR main auctions is the limited information that banks have on the frictional factors like changes in GoI cash balances, because of which they are unable to forecast liquidity for 14 days. This marked reluctance of banks to part with surplus liquidity for longer tenors was also one of the reasons that the Reserve Bank had to conduct a relatively higher number of fine-tuning VRRR operations to absorb excess liquidity from the system.

Chart V.7: Bid-to-Cover Ratio during CY2023 and CY2024 and Chart V.8: VRRR Main Operations in 2024 - A Snapshot

V.29. A large number of fine-tuning operations, in general, increase the uncertainty about the Reserve Bank’s liquidity operations. Further, as fine-tuning operations are conducted at the discretion of the Reserve Bank, there is uncertainty in the market about the quantum and timing of operations. Consequently, it is observed that when fine-tuning operations were announced at a short notice i.e., on the same day of the operation, the short-term market rates adjusted to ex-ante liquidity conditions without accounting for Reserve Bank’s operations and may have resulted in further lowering the participation in fine-tuning operations.

V.30. Unlike VRRR auctions, higher average bid-to-cover ratios observed in both main and fine-tuning VRR auctions conducted during the last two calendar years indicate that banks actively participated in these auctions, irrespective of tenors (Chart V.7). It is pertinent to mention here that bid-to-cover ratio in VRR auctions inter-alia depends on total notified amount of auctions vis-à-vis the prevailing liquidity conditions. For instance, a VRR auction with a significantly lower (higher) notified amount than the actual liquidity requirement of the banking system may result into a higher (lower) bid-to-cover ratio.

V.31. From an analysis of VRR auctions conducted during the period January 16, 2025 to March 31, 2025, when the system liquidity was largely in deficit, it is observed that daily VRR auctions16 saw higher participation than the 14-day VRR main operations. While the average bid-to-cover ratio for daily VRR operations conducted during the said period was 0.80, it was 0.55 for VRR main operations. Banks’ preference to borrow funds in daily VRRs over 14-day VRR main operations can be attributed to frictional factors discussed earlier, which make it difficult for them to forecast liquidity over 14-day period.

Tenor of Liquidity Operations

V.32. As discussed in the previous section, challenges faced by banks in forecasting the liquidity position for the entire fortnight results into their lower participation in 14-day main operations, thus undermining the efficacy of 14-day operations in managing transient liquidity. Moreover, frictional factors like changes in GoI cash balances on account of quarterly advance tax receipts, monthly goods and services tax (GST) receipts, salary and pension related expenditure, etc. lead to significant variations in the system liquidity within the fortnight. Such intra-fortnight variations in the system liquidity can be better managed through shorter tenor operations instead of 14-day operations. Further, the choice of tenor of operations should ensure the optimum level of system liquidity which enables alignment of the operating target to the policy repo rate, to the extent possible.

V.33. Therefore, the Group recommends that 14-day VRR/VRRR auctions may be discontinued as the main operation. Instead, the transient liquidity may be managed primarily through 7-day repo/ reverse repo operations and other operations of tenors from overnight up to 14-days at the discretion of the Reserve Bank based on its assessment of the system liquidity requirement.

V.34. Further, to reduce uncertainty in the market about the tenor, quantum and timing of the repo/reverse repo operations, the Group felt that it is desirable for the Reserve Bank to provide sufficient advance notice to market participants, at least by one day, while conducting any such liquidity operation. The Group has also noted the implementation of standard time window from 9:30 AM to 10:00 AM for pre-announced operations as per the recommendations of the “Report of the Working Group on Comprehensive Review of Trading and Settlement Timings of Markets Regulated by the Reserve Bank” (Chairman: Shri Radha Shyam Ratho, RBI, 2025).

V.35. The Group observed that there may be situations in which the evolving liquidity conditions may warrant conduct of liquidity operations on the same day of announcement. The Group recommends that in such circumstances, the Reserve Bank may conduct repo/reverse repo operations announced on the same day.

Variable or Fixed Rate operations

V.36. Liquidity management operations are generally conducted either at a fixed-rate on full-allotment basis or through variable rate auctions for a specific quantum decided by the central bank. In the case of fixed-rate full allotment-based liquidity operations, liquidity operations of a particular day would be determined by requirement of each bank, while variable rate auctions allow the central bank to conduct liquidity operations based on its assessment of the system liquidity.

V.37. While the former approach provides assurance to each individual bank about their day-to-day liquidity requirement, the latter depends on the assessment/forecasting of system liquidity by the central bank and hinges on an efficient money market for equilibrating the supply and demand of reserves in the system. However, in the former approach, unlimited liquidity from the central bank on fixed-rate full allotment basis has moral hazard issues and would also adversely impact the inter-bank activity in the overnight segment.

V.38. Although the LAF operations are not meant for price discovery, bids received in variable rate auctions can be a good signal for assessing the true extent of funds required from (to be deployed with) the central bank. Moreover, variable rate auctions provide the central bank enough flexibility to decide on auction cut-offs and the amount which would make liquidity management more nimble and agile rather than being constrained by the fixed rate.

V.39. In view of the above, the Group recommends continuation of variable rate auction mechanism for conducting repo and reverse repo operations under the LAF. As liquidity provided or absorbed would be based on requirement of the entire banking system rather than that of individual banks, it is likely to encourage inter-bank activity during the tenor of the auction.

V.40. Further, repo/reverse repo operations of tenor overnight to 14-days are expected to handle changes in system liquidity due to frictional factors. However, in some scenarios, the liquidity mismatches may extend for a longer term. For handling such liquidity mismatches, the Group recommends that the repo/reverse repo operations of appropriately longer tenor may be conducted through variable rate auctions. The availability of such instruments would provide the necessary liquidity assurance to the market and would also help in minimising frequency and quantum of shorter tenor operations.

V.41. The Reserve Bank meets the demand for durable reserves by altering the Net Domestic Assets (NDA) and Net Foreign Assets (NFA) on its balance sheet. While the outright forex operations may be conducted with a different objective, it affects the system liquidity on a durable basis. The extant LMF includes an array of instruments to address durable liquidity surplus or deficit. OMOs along with other instruments such as the long-term variable rate repo/reverse repo operations and FX swap auctions are used to inject/absorb system liquidity on a durable basis. The Group noted that instruments under the extant LMF are sufficient for meeting the durable liquidity needs of the system and hence, does not recommend any change at this stage.

Standalone Primary Dealers

V.42. SPDs are highly leveraged institutions which also depend on the overnight segment of money market to meet a part of their funding requirements. Given that WACR continues to be the operating target of the monetary policy, it is imperative that all members of the call money market also have access to the Reserve Bank’s liquidity facilities for effective monetary policy transmission. SPDs are the largest borrowers in the call money market contributing about three-fourths of the total borrowing.

V.43. Recognising their important role in the primary and secondary market for Government securities, SPDs were allowed to participate directly in all overnight liquidity management operations, except in the MSF. Besides, SPDs have also been provided with Standing Liquidity Facility (SLF) to borrow funds from the Reserve Bank against eligible Government securities, at the policy repo rate, repayable within a period of 90 days. From March 2025, they have also been allowed access to all repo operations irrespective of tenor.

V.44. While SPDs have recently been allowed to participate in all repo operations irrespective of tenor, given the limited stock of free Government securities with them, they are able to participate in the overnight repo auctions only when these are announced well in advance (i.e., at least one day prior to the auction date). Also, as the overnight variable rate auctions are conducted based on the assessment of evolving liquidity conditions, there is uncertainty regarding their timing and quantum which further makes it difficult for SPDs to plan their participation in these auctions. Therefore, giving advance notice of operations of the Reserve Bank will enable SPDs to improve their participation in LAF operations.

V.45. The MSF has been made available to banks to meet any unanticipated shortfall in maintenance of their daily cash reserve requirements. With RTGS/NEFT available 24x365, banks may have unforeseen payment obligations beyond market hours. However, the Group does not see any unforeseen liquidity requirements for SPDs after the close of market. Further, any emergency liquidity needs can be met by SPDs in the now extended call market timings with effect from July 01, 2025. Therefore, there may not be any need to extend MSF or provide another similar overnight emergency funding facility to SPDs.

Averaging of Reserve Requirement

V.46. Mandatory reserve requirement in the form of CRR and payments system obligations are major determinants for demand for reserves on any particular day. The averaging of reserve requirement over the maintenance period allows banks to use excess reserves from one day to offset shortfall on other days.

Chart V.9: Averaging of Reserve Maintenance over a Reporting Fortnight

V.47. In India, the daily minimum reserve requirement was prescribed with a view to providing flexibility to banks in choosing an optimum strategy of holding reserves depending upon their intra-fortnight cash flows. Within the reporting fortnight, banks flexibly optimise their daily maintenance levels based on a cost-benefit analysis of interest rate expectations vis-à-vis rates on standing facilities (Chart V.9.a). The daily minimum reserve requirement was enhanced from 70 per cent of NDTL (effective since December 2002) to 99 per cent in July 2013 but subsequently reduced to 95 per cent in September 2013 and further to 90 per cent in April 2016. During COVID-19 pandemic, it was lowered to 80 per cent in March 2020 in view of hardships faced by banks due to social distancing of staff and consequent strain on reporting requirements but subsequently reverted to 90 per cent by September 2020. If the daily minimum requirement is very high, it constrains the flexibility of banks during the reserve maintenance period. For instance, the intra-fortnightly variation (across weeks) in reserve maintenance was negligible when the daily minimum was prescribed at 99 per cent after the taper tantrum as compared with significant frontloading of reserve maintenance in the first week vis-à-vis the second week when daily minimum balance was set at 70 per cent (Chart V.9.b).

V.48. Empirical evidence shows that banks build precautionary reserve balances to honor payment obligations of 24x365 payment systems in India, which may have pushed neutral system liquidity higher. In this connection, there is a view that some reduction in the 90 per cent daily maintenance requirement is warranted as it may provide further room for banks to effectively manage their liquidity over the maintenance period. However, on the flip side, reducing the minimum daily balance requirement from the extant 90 per cent may induce larger volatility in WACR, especially at the end of fortnights. Increased volatility in the operating target adversely affects the smooth transmission of monetary policy. The resultant increased variability in the daily CRR maintenance of individual banks may also pose challenges to the Reserve Bank’s liquidity forecasting. It has also been observed that banks rarely maintain daily reserve balances below 95 per cent of the prescribed CRR. Thus, the extant 90 per cent requirement is not a binding constraint on banks.

V.49. The Group recognises that the present minimum requirement of maintaining 90 per cent of the prescribed CRR on a daily basis has helped avoid bunching of reserve requirement by individual banks. Accordingly, the Group recommends the Reserve Bank to retain the extant daily minimum requirement of 90 per cent of the prescribed CRR.


VI. Summary of Recommendations

VI.1. The Group recommends that the overnight Weighted Average Call Rate may continue as the operating target of monetary policy. The Reserve Bank may, however, continue to keep track of rates in other overnight money market segments to ensure orderly evolution of money market rates and smoothen transmission.

(Para V.12.)

VI.2. The Group recommends continuation of the existing corridor system with policy repo rate at the middle of the corridor. The corridor would remain symmetric, with SDF and MSF, which are 25 bps on either side of the policy repo rate, acting as the lower and upper bounds of the corridor system, respectively.

(Para V.22.)

VI.3. The Group recommends that 14-day VRR/VRRR auctions may be discontinued as the main operation. Instead, the transient liquidity may be managed primarily through 7-day repo/ reverse repo operations and other operations of tenors from overnight up to 14-days at the discretion of the Reserve Bank based on its assessment of the system liquidity requirement.

(Para V.33.)

VI.4. To reduce uncertainty in the market about the tenor, quantum and timing of the repo/reverse repo operations, the Group felt that it is desirable for the Reserve Bank to provide sufficient advance notice to market participants, at least by one day, while conducting any such liquidity operation.

(Para V.34.)

VI.5. The Group observed that there may be situations in which the evolving liquidity conditions may warrant conduct of liquidity operations on the same day of announcement. The Group recommends that in such circumstances, the Reserve Bank may conduct repo/reverse repo operations announced on the same day.

(Para V.35.)

VI.6. The Group recommends continuation of variable rate auction mechanism for conducting repo and reverse repo operations under the LAF.

(Para V.39.)

VI.7. For handling liquidity mismatches of longer term, the Group recommends that the repo/reverse repo operations of appropriately longer tenor may be conducted through variable rate auctions.

(Para V.40.)

VI.8. The Group noted that instruments under the extant LMF are sufficient for meeting the durable liquidity needs of the system and hence, does not recommend any change at this stage.

(Para V.41.)

VI.9. The Group recommends the Reserve Bank to retain the extant daily minimum requirement of 90 per cent of the prescribed CRR.

(Para V.49.)


Annex: Monetary Policy Operating Frameworks – Cross-country Perspectives

Sr. Country Key Policy Rate
(Maturity in Days)
Operating Target
(Days)
Standing Facilities Corridor Width Bank Reserve Main Operation Discretionary Operations Counterparty
Required Period Instrument Maturity
(in days)
Frequency Pricing    
1 Australia Target Cash Rate (1) Unsecured inter- bank cash rate (1) Lending, deposit 35 bps Yes17 Daily Repo/ Reverse Repo Typically, 4 weeks or less18 Weekly Fixed Outright Members of RBA’s RTGS system
2 Brazil Selic Rate - Target overnight rate (1) Collateralised Selic rate - overnight rate (1) Lending, deposit 70 bps Yes Two19 weeks Repo/ Reverse Repo 1-360 As required Auction Interest bearing voluntary deposit Primary Dealers and Financial Institutions
3 Canada Target Overnight Rate (1) Collateralised overnight transactions (1) Lending, deposit 30 bps No NA Reverse Repo Repo 1 Daily20

As required
Fixed Auction Overnight standing repo Primary Dealers
4 China Benchmark interest rates Excess Reserve and short-term interest rate Lending NA Yes 10 days Repo/ Reverse Repo Generally, 7, other maturity ≤ 1 year Daily Auction Medium-term Lending Facility (MLF), Pledged Supplementary Lending(PSL) Primary Dealers
5 Euro System Deposit Facility Rate (1) Short term interest rates (not explicit) Lending, deposit 40 bps Yes Around 6-7 weeks Main Refinancing Operation

Long Term Refinancing Operations (LTRO)
7

3 months
Weekly

Quarterly
Fixed

Varies
TLTROs, fine-tuning operations, structural operations, Outright Monetary Financial Institutions comprising Credit Institutions, Money Market Funds, and Central Banks
6 Indonesia Bank Indonesia (BI) 7-day Reverse Repo Rate (7) Unsecured Inter-bank overnight Rate (1) Lending, deposit 150 bps Yes Two weeks Reverse Repo 1-week to 12 months Daily Auction Repo, Term Deposit, Term Repo, Outright Banks registered as participants in monetary operations
7 Japan21 Short term policy interest rate (1) uncollateralized overnight call rate (1) Lending, deposit 25 bps Yes 1 month Reverse Repo

Repo
Up to 1 year

Up to 6 months
As needed Auction (i) Funds – supplying operations against pooled collateral (ii) Loan support program Financial Institutions, securities finance companies, and Tanshi companies22
8 Korea Base Rate (7) Overnight call rate (1) Lending, deposit 100 bps Yes 1 month MSB23

RRP

MSA24
(i) MSBs (14 day – 3 years);

(ii) RRPs (1-91);

(iii) MSAs (1-91)
(i) 2 per week;

(ii) 1 per week;

(iii) 1 per week
Auction Outright transactions, Securities lending /borrowing25 Banks, investment & securities companies
9 Malaysia Overnight Policy Rate (1) Average overnight inter-bank rate (1) Lending, deposit 50 bps Yes Two weeks Direct borrowing, Repo, Reverse repo,       CB Bills26, FX swaps 1 day – 3 years Daily Auction Term deposit Financial institutions that are interbank participants
10 Mexico Monetary Policy Rate (1) Collateralised overnight inter- bank rate (1) Lending, deposit - No NA Repo and Deposits 1 - 30 days Daily Auction Voluntary deposit, Liquidity deposit Commercial and development banks
11 Philippines Overnight Reverse Repo Rate (1) No formal target Lending, deposit 100 bps Yes One week Reverse Repo 1-day Daily Fixed rate Term deposit, Outright Banks and Non-Banks with Quasi-banking Functions
12 Switzerland SNB policy rate (1) Secured short-term Swiss franc money market rates (1) Liquidity – shortage financing facility NA Yes One month Repo

SNB Bills
1 week to 1 year

Up to 1 year
As required Fixed rate

Variable rate
Outright Fx purchase and sale, Fx swaps All domestic banks with sight deposits at the SNB; other domestic participants in the financial market, as well as banks that are domiciled abroad fulfilling eligibility criteria
13 Thailand Policy repo rate (1) Short-term money market rate (1) Lending, deposit 100 bps Yes Fortnightly Reverse repo

CB bills & bonds

FX Swap
1,7,14D and 1M

3 months to 2 years

up to 1 year
Daily 1-2 times per week Discretionary Note27

Variable

Auction
Outright purchase of public sector debt securities Primary Dealers
14 UK Bank Rate (1) Short-term money market rates Collateralised lending, unsecured deposit 50 bps No28 6 - 8 weeks Short Term Repo 7 days Weekly Fixed rate Outright purchase of govt. bonds, corp. bonds, Term Funding Scheme Banks, Broker-Dealers, Building Societies, CCPs
15 US Federal Funds Rate Target range for federal funds rate Lending29 Deposit30 25 bps No NA FIMA Repo, Standing Repo, Reverse Repo, 1-day to 90-day Daily Fixed Outright Primary Dealers, Money Market Mutual Funds, Depository Institutions, Govt. Sponsored Enterprises, Depository Institutions
Source: Bank for International Settlements (BIS), Respective central bank websites. Note: NA: Not Applicable

1 The share of the call money market in the overnight money market volume has declined from 16 per cent in FY 2010-11 to 2 per cent in FY 2024-25.

2 The Reserve Bank’s LAF consists of SDF, MSF, repo and reverse repo operations.

3 BIS Markets Committee compendium (https://www.bis.org/mc/currency_areas/in.htm)

4 RTGS was made available 24x365 with effect from December 14, 2020

5 https://doe.gov.in/files/public-finance-state-cna-sna-document/Letter_Dated_13_07_202_To_ALL_Chief_Secretaries_Principal_Secretaries.pdf

6 In terms of the Reserve Bank’s press release “Participation of Standalone Primary Dealers in Variable Rate Repo operations

7 System Liquidity as defined by net borrowing under LAF adjusted for excess reserves maintained by banks.

8 During COVID-19 pandemic, various liquidity measures including Long Term Repo Operations (LTROs), Targeted LTROs (TLTROs), Special LTROs (SLTROs), G-sec Acquisition Programme (G-SAP) etc., were adopted by the Reserve Bank to infuse long-term liquidity into the system, in an attempt to ensure normal functioning of financial markets.

9 Presently, eligible banks can draw funds under MSF against their excess SLR holdings and additionally up to two per cent of their respective NDTL outstanding at the end of the second preceding fortnight by dipping into the prescribed SLR , in terms of the Master Direction - Reserve Bank of India [Cash Reserve Ratio (CRR) and Statutory Liquidity Ratio (SLR)] Directions - 2021 issued by Department of Regulation of the Reserve Bank.

10 Only four major central banks (Canada, Brazil, Mexico, and Switzerland) have shifted to overnight secured rate (repo) as the operating target.

11 Currently, SPDs have access only to SDF, overnight reverse repo operations and all repo operations, irrespective of tenor.

12 A wider corridor is indicative of tighter monetary policy implying that central bank standing facilities are costly. For a detailed discussion on the optimal width of the corridor, see Chapter 4 on Operating Procedure of Monetary Policy of Report on Currency and Finance, 2020-21.

13 As a volatility measure, the EWMA is an improvement over simple variance as it assigns greater weight to more recent observations. Thus, EWMA expresses volatility as a weighted average of past volatility where the weights are higher for more recent observations.

14 Moreover, lower volatility in the overnight rates reduces term premium and hence lower medium and longer-term rates (Carpenter and Demiralp, 2011).

15 In the existing LAF corridor, the SDF rate is 25 bps lower than the policy repo rate. Further, banks can place their bids at rates lower than the policy repo rate while participating in VRRR auctions, i.e., if repo rate is at 5.50 per cent, banks can bid up to 5.49 per cent. Thus, parking excess reserves under SDF instead of participating in VRRR auctions may result in an opportunity cost of up to 24 bps.

16 As announced vide the press release “RBI to conduct Daily Variable Rate Repo (VRR) Auctions” dated January 15, 2025, the Reserve Bank conducted variable rate repo (VRR) auctions from January 16, 2025 on all working days in Mumbai with reversal taking place on the next working day. These auctions were discontinued with effect from June 11, 2025, vide the press release dated June 9, 2025.

17 Reserve Bank of Australia’s reserve requirements are not a Required Reserve Ratio (they are not set as a per cent of the financial institution’s liabilities). The reserve requirements are set annually as an Australian Dollar (AUD) amount per financial institution with the aim of ensuring that each institution has sufficient funds to settle AUD transactions after business hours. The amount is set taking into account the historical pattern of each institution’s AUD transactions.

18 Longer terms may be offered if warranted by market conditions

19 Two weeks for demand deposits / 1 week for savings and time deposit

20 Extra rounds of reverse repo as required

21 All details pertaining to the Bank of Japan are as per the information available on the BIS website except the policy rate, operating target and the corridor width which have been updated as per the recent policy decision made on January 25, 2025.

22 Money market brokers

23 MSBs: Monetary Stabilisation Bonds, issued by Bank of Korea, used as a structural adjustment tool whose policy effects are long lasting.

24 MSA: Monetary Stabilisation Account, introduced in October 2010, is a market-friendly term deposit facility. It is mainly used to fine-tune the reserve levels and to cope with unexpected changes in reserve supply and demand.

25 Outright transactions are conducted only limitedly, since they are employed to provide or absorb liquidity permanently, and they therefore directly affect long term market rates. For this reason, securities transactions focus mainly on Repurchase Agreement (RP) transactions (mostly with 7-day maturities). Meanwhile, following the revision of the Bank of Korea Act in August 2011, the Bank can operate a securities lending and borrowing facility, which is enabling the Bank to not only enhance the efficiency of the liquidity management through flexible expansion of the volume of its RP sales, but also cope more effectively in the case of financial turmoil such as a credit crunch.

26 The Bank Negara Malaysia (BNM) can issue central bank bills; Bank Negara Monetary Notes (up to 3 years) and Bank Negara Interbank bills (up to 1 year).

27 1D: Fixed rate; >1D: Variable rate, indexed with the policy rate

28 In an explanatory note published in August 2022 the Bank of England set out that it was minded to use a variant of the extant floor system.

29 Discount window, FIMA repo facility, Standing repo facility

30 ON RRP facility and Foreign repo pool

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Motivation and Benefits

3.1 General

3.1.1 In general, innovation in financial products and services can improve economic performance by (i) meeting demands for completing the markets with expanded opportunities for risk sharing and risk pooling. Completing the markets refers to the class of securities being expanded by securities that provide access to risk-return combinations that previously were not available to investors; (ii) lowering transaction costs or increasing liquidity; (iii) reducing agency costs caused by asymmetric information and costly and incomplete monitoringi.

3.1.2 Securitisation in the past has enabled banks and thrifts to cope with disintermediation (disappearance of low cost, fixed rate deposits and high quality, higher yielding loans) by reshaping their intermediary role and turning them from spread banking to conduit banking, deriving their income from originating and servicing loans ultimately funded by third parties. The requirements for capital adequacy in recent years have also motivated the FIs to securitise. Further, lack of access to outside capital especially in current macro-economic scenario when credit rating for many developing countries has been downgraded, is another major motivating factor. On demand side, investors viewed securities issued in securitised transactions as having desirable risk characteristics and greater spread over US Treasury obligations (a benchmark rate) than securities of comparable risk.

3.1.3 Globalisation, deregulation of financial markets and the surge in cross border activities have increased competition among financial institutions and have created opportunities for financial engineering. Securitisation increases lending capacity without having to find additional deposits or capital infusion. The FI gets more visible to the outside world and investors through the process of securitisation. The process of origination, underwriting, loss recovery, servicing etc. start getting attention of investors, rating agencies and other outside parties. This leads to self-examination and careful business decisions. Securitisation facilitates specialisation as has been seen in US securities market. Loan originations are often geared to meet another institutions underwriting standards. Loan servicing may be provided

by a third institution, and assets may be sold to yet another party (SPV). For bad debts, an outside service agency's services may be taken. A liquidator may dispose off assets. Once these functions are separable, costs and efficiencies become transparent. FIs retain those functions / services that have perfect fit with core competence or operational advantage of the organisation.

3.1.4 FIs should look to securitisation as an opportunity. First, they can maximise their distribution capacity and raise their turnover of assets rather than the volumes of assets. The result can be a series of fee income rather than one interest spread. Second opportunity is to increase shareholders value substantially. By unbundling the balance sheet and selling off assets, FIs will be left overcapitalised. An obvious solution is to repurchase equity (if covenants permit) and enhance ROE substantially. Primary dealers in Government securities market, whose stock in trade are Government securities, can unbundle the interest coupons and securitise the same. The main advantages are in the form of capital relief, capital allocation efficiency, and improvement in financial ratios, etc.

Balance Sheet Effects:

3.2 Capital relief

FIs as Originators are required to maintain minimum capital to risk-weighted assets ratios (CRAR). In a true securitisation process assets are taken off the balance sheet of the Originator. To that extent, CRAR is not required to be maintained. Other Originators may be restricted by their indenture covenants or by regulators from securing debt beyond a specified level. Securitisation reduces the total cost of financing by giving capital relief. The cost of capital coverage (CCC) for the assets in question is eliminated since the assets are removed from the balance sheet. This cost represents the incremental additional cost of equity over the cost of 100% debt financing. We assume that the FI is not over-capitalised and any funding of assets by traditional balance sheet finance requires the Originator to maintain the proportion of debt and equity constant before and after the financing. In other words, CCC is the weighted average cost of capital (WACC) minus cost of debt.

Securitisation reduces the cost of capital in the following way:

  1. investors benefit from access to markets where previously this was not possible
  2. liquidity lowering the required rate of return

3.3 Capital planning

Capital to total assets can be increased either by (i) raising tier I capital or (ii) raising tier II capital or (iii) securitisation. If Tier I capital is issued, share prices may go down. There are limits for issuing tier II capital. In the case of securitisation, banks may provide funds for Cash Collateral Account to meet loan losses out of capital as a method of internal credit enhancement. They may have two options:

Option I

Two tranches: AAA at 16 BP over LIBOR and A at 40 BP above LIBOR

Loan loss: 2%

Option II

Three tranches: AAA at 16 BP, A at 40 BP and BBB at 80 BP above LIBOR.

Loan loss: 1%.

Second option is more expensive in debt term, but cheaper in equity terms.

Capital requirement

Banks and other financial service institutions ("regulated institutions") are required to maintain certain minimum capital-to-riskweighted-assets ratios pursuant to the Basle Committee guidelines applicable to them. Basle guidelines on capital requirements may probably cause the FIs in western countries to consider: (i) decrease commercial, credit card, auto loans with 100% risk weightage (there is more incentive for these assets than the mortgage loans with 50% weightage) and securitise them; (ii) invest the funds thus generated in lower-weighted Treasuries (0 percent weight) or agencies (20 percent).

Securitisation leads to capital relief, which in turn improves leverage. The improvement in leverage can improve the Return on Equity (ROE) of a company as is illustrated belowii.

Table 1: Capital Requirement

Comparison between two banks #1 and #2

Leverage = Assets/ Equity

Total assets of bank #1 and #2 are $30 each.

Net Interest Margin or return on assets (ROA) = 1% or 0.30

US$

Bank # 1
Equity=1.00

Bank # 2
Equity=0.25

Leverage
ROE

30/1=30
0.30/1.00=30%

Leverage
ROE

30/0.25=120
0.30/0.25=120%

Implication:

The higher the leverages, the higher the ROE even with the same ROA (1% in the above case).

 

There is limitation beyond which it is not prudent for banks to increase the leverage. In the above illustration, suppose the local bank Regulators impose the loan loss provision requirement of say average 2% of total assets or 0.60, which has to be provided from capital, then Bank #1 will survive, but Bank #2 will have negative capital of 0.35.

Income Statement Effect

Securitisation can have the following income-related effects:

Recognising profits

When the assets held in investment account (as against the trading account) are sold consequent upon a fall in market interest rates, profits are recognised. If these assets were not securitised, the same would continue to be shown at the book value till maturity or till they are sold.

Changing the timing of income

Securitisation helps in adjusting the receipt of cash flows as per the needs of the interested party. The sequential tranches can help deferring the receipt of principal to a later date by a particular party, which can help in tax planning. The cash flows can be compared to rain storms and water pipes delivering water to a city. The benefits of any fixed volume of water are determined as much by how it is controlled as by its sheer volume. Securitisation is like a water works system for cash flows. It allows effective direction and control of flows to specific purposes. The structure delivers the proper flows in the right quantities at the right timings to meet these objectives.

 

Raising funds at cheaper rates

Improvement in credit rating reduces the fund raising.

 

One time fee income:

Income may be improved because the institution can charge one-time fee for processing loans and also can serve as administrator for those loans, which are securitised. This improves return on assets (ROA).

Influence on financial ratios

As illustrated below, cash generated through securitisation has different repercussions on the balance sheet, depending upon the strategy of the company for its capital structure and its appetite for increasing or decreasing leverage. In the following illustration, the impact of securitisation on the financial ratios of bank XYZ is given.

Table 2: Financial ratios

Assumptions: (i) Receivables for auto loans are sold at par; & (ii) Loan to Value ratio is 100% Original B/S (US $)

Assets

Liabilities

Receivables for auto loans
Consumer loans

100
100

Debt
Equity

100
100

Debt: Equity ratio = 1:1

Scenario I

When XYZ borrows 100 (not "true sale"), secured by its receivables for auto loans, the B/S will undergo change as under:

Assets

Liabilities

Cash
Receivables for auto loans
Consumer loans

100
100
100

Debt
Equity

200
100

Result:

  • debt equity ratio worsens to 2:1
  • debt equity ratio remains same if 100 realised is used to pay old debt

 

Scenario II

When receivables for auto loans are sold (‘True sale’)

Assets

Liabilities

Cash
Consumer loans

100
100

Debt
Equity

100
100

Result:

  • debt equity ratio is unchanged at 1:1
  • B/S size is unchanged

Scenario III

In Scenario II, when major part of the fresh cash received is utilised to pay off debt

Assets

Liabilities

Cash
Consumer loans

10
100

Debt
Equity

10
100

Result:

  • debt equity ratio improves dramatically
  • B/S size is reduced

Other Benefits

3.7 Providing Market Access

Borrowers are able to have access to markets in a better way through securitisation: (i) non-investment grade institutions in EMs can fund themselves at investment grade pricing; (ii) assets backing a security paper are subjected to stress by the rating agencies to arrive at the level of credit enhancement required, providing added comfort to the investor. This improves the access of the FIs to a wide range of investors; (iii) the improved rating can help FIs in EMs to get capital for longer tenure than normally available; and (iv) low rated Originators can have access to cheaper funds with enhanced rating which may include piercing of the sovereign ceiling of rating in certain cases. A sovereigns rating on its foreign currency obligations is normally regarded as a ceiling on ratings for other issuers domiciled in the country. Sovereign default could force all other domestic issuers to default as a mean of avoiding own default. However, securitisation through structuring of a particular set of assets and various credit enhancement devices may be able to pierce through this sovereign ceiling. Historical evidence suggests that sovereign foreign currency defaults don't always lead to defaults in private sector. When New York City defaulted in the 1970s, companies from the Big Apple did not. Factors like the strategic importance of an FI to the country may persuade Government to allow certain issuers to continue servicing their debt even when rigorous exchange controls are in place. Geographical diversification, international affiliations, and support agreements may accord some institutions to perform better than the sovereign may.

3.8 Overcoming constraints of Market Segmentation

A market segment is an identifiable group of investors (or purchasers) who purchase a product with particular attributes that are distinct from the attributes of alternative investments. Investors who prefer a firm with a particular capital structure strictly because of their own risk preference are able to avoid transaction costs of personal leverage by simply investing in a firm that already has their preferred amount of leverage5. Different tranches in securitisation overcome constraints of market segmentation. Securitisation reallocates risks to the segment of the market most willing and able to manage them, such as by obtaining a surety bond, letter of credit, or dividing the securities issued into a larger senior class, which is sold at a lower yield than could be achieved without segmenting the asset's risk, and a smaller subordinated class, which is either retained by the seller, or sold at a higher yield than the senior class. Unlike the conventional capital markets, securitisation allows borrowers of all sizes and credits to access capital markets and thus remove the constraints of market segmentation. Investors, who are not bankers, can't originate loans and can't get exposed to loans. For example, an insurance company normally invests only in bonds, treasury bills etc. Securitisation helps it to invest in ABSs backed by commercial loans, an opportunity, which was never available earlier. Similarly, other segments of market are able to have access to a variety of instruments: investors having tolerance for interest rate risk can get long term paper, those who want to match short term liabilities can pick up short-term paper. Sequential issues meet the appetite of other types of investors.

3.9 Strategic tool

Securitisation benefits the FIs in different ways by: (i) providing strategic choices; (ii) reducing funding costs; (iii) developing core competencies in certain areas. For example, some institutions specialise in originating and servicing, not financing at all. Other institutions expand business volume without expanding their capital base in the same proportion. The process helps in identifying cost pools of various activities in the value chain. As can be seen from Figure 2, securitisation is changing the horizons of traditional banking significantly:

Figure6 2: Traditional banking and Securitisation

Many new lines of business grow out of securitisation - insurance of assets, clearance services, custodial services and master servicing of securities etc. Depending upon the core competence and trade off between costs and benefits, institutions may like to retain or divest of some of these activities. Institutions may develop competitive advantage through more efficient marketing, tighter credit monitoring, lower cost servicing, higher volumes (automobiles, credit cards etc.) and other ways to outperform competitors.

3.10 Liquidity

(i) Fund raising through securitisation is independent of the Originators rating. The market for securities is more efficient than for bulk asset sales as the latter is illiquid. Many banks are trapped in a situation where they can't rollover their debt due to downgrading of the ratings of the issuer below investment grade consequent upon the changes in economic environment. This happens when long term assets are being financed by short-term liabilities (CP. etc.) which are rolled-over from time to time. Securitisation enables FIs to increase the rating of debt much higher than that of the issuer through intrinsic credit of the assets themselves. This enables the FIs to obtain funding which was not feasible earlier. The funds raised by some of the banksiii in EMs are examples in the point.

(ii) The liquidity provided by securitisation makes it an extremely powerful tool, which can be used by management to adjust asset mix quickly and efficiently. The risks in an asset portfolio can be divided and apportioned so that some risks are transferred while others are retained.

(iii) Liquidity is also increased through fractionalised interest that is marketable to a broader range of suppliers of capital.

3.11 Risk Tranching / Unbundling

(i) A securitised transaction is structured to reallocate certain risks inherent in the underlying assets such as prepayment risk and concentration risk. With reduced or reallocated risks and greater liquidity, securities are more appealing to a wider range of purchasers (conform the market segmentation theory as explained in para 3.8) and, consequently, the yield required to sell them will be lower.

(ii) Securitisation can modify the risk exposure of investors to various risks by creating securities that allocate these risks according to specific rules. The institution can then sell the securities, which have risk characteristics not suitable to the organisation and keep those with risk profile matching the overall mission of the organisation. An example is the practice of subordinating one tranche of a security to another for credit enhancement. A security may be divided into two tranches, A class and B class, the former giving lower yields but having priority over claims than B class security.

(iii) Investors in ABSs have typically no recourse against the issuers. In a perfect securitisation process, true sale is involved and issuers can use SPVs to transfer, for instance, interest rate risk and credit risk to investors. Securitisation can help FIs manage interest rate risk in two ways. While variable rate loans and sale of loan participations enable a lender to share interest rate risk with borrowers or other FIs, asset securitisation may, in certain cases, permit a lender to remove the asset from its portfolio altogether, thereby shortening the portfolios average maturity, and eliminating all interest rate risk associated therewith. Moreover, as buyer of MBS and ABS, FIs can select securities with shorter weighted average lives to match their short-term deposits. Thus, banks and thrifts have been big purchasers of "fast pay" tranches. Credit risk is transferred to credit enhancers. Credit risk is transferred in full if the issuer does not retain an interest in the assets. It is transferred in part if an issuer invests in an SPV (which is normally not the case) or retains a subordinate interest in it.

3.12 Asset-Liability Management

Some FIs in the EMs are not in a position to raise long-term international borrowings due to various limitations including the size of the institution, the sovereign limitation, etc. Securitisation helps in improving the rating for particular deal much above the institutions rating and enables the institution to raise funds for a longer period. This facilitates in matching the tenure of the liabilities and the assets.

Securitisation allows flexibility in structuring the timing of cash flows to each security tranche. In general, securitisation provides a means whereby custom or tailor made securities can be created. For example, a typical security issuer might wish to shorten the duration of a portfolio of mortgage loans. The liabilities against which mortgage loans are funded may have shorter duration than the assets. To minimise the gap mismatch, the issuer bank may create two classes of securities from mortgages sequential pay securities i.e. the second security receives only interest until the principal and interest for the first security has completely been paid. The second security receives principal and interest only thereafter. Selling the second one and retaining the first one shortens the duration of its asset portfolio.

Securitisation also segments funding and interest rate risk so that it can be tailored and placed amongst appropriate investors. For example, in the mortgage area by virtue of relationship with their customers, banks and housing finance companies are best positioned to originate loans. Mortgage loans are usually for long tenures (between 15-20 years). Banks typically do not have access to such long tenure funds. On the other hand, investors such as pension funds and life insurance companies have long term funds, which require consistent yield. MBS thus enables the financial system to match the funding profile and thereby reduce aggregate risk in the financial system. The other risk in the mortgage finances is the incidence of early payment that arises as borrowers foreclose their loans due to various reasons. Creating multiple tranche from the common pool of receivables and thereby providing instruments, which have different types of early payment risks, can create structures of MBS to fine tune this risk. In addition, structures can be created using interest rate swaps etc. to ensure that the interest rate risks are passed on to natural counterparts.

3.13 Diversification of assets

Regulators in some countries have imposed ceilings for exposures of FIs to a single / Group of borrowersiv as illustrated below.

 

India

25%/50% of capital and free reserves for banks for single /Group exposures

HK

2% of capital

Indonesia

20% of capital for groups of affiliated borrowers; 10% for a single person

Malaysia

30% of capital

Brazil

30% of net worth

 

The objective may be to reduce concentration of risk and also to make credit available to larger sections of society. Securitisation helps in the diversification of the loan portfolio beyond a few companies, geographical locations or even industries. FIs may take loans to certain customers off balance sheet in order to be able to lend new funds to those customers and still maintain the credit exposure limits.

3.14 Systems/Reporting

Securitisation provides the incentive to an FI to manage its loan portfolio better and keep better track of delinquencies and put more pressure on them to pay, in order to keep the cost of future credit enhancement low. The portfolio has to be made more transparent to rating agencies and the investors. This permits easier mapping of internal risk codes with the external agency letter ratings needed to set pool risk ratings and enhancement levels. One important operational concern that new issuers of ABSs face is that of inadequate historical data on the assets and their performance. Data on loan payments etc. are many times not considered important for the ongoing maintenance of asset portfolio. These involve heavy costs for FIs in the EMs. The need to document the policies and procedures for originating, monitoring and servicing the assets to meet the requirements of the rating agencies helps FIs tone up their systems. The responsibility / accountability of FIs extends from equity holders to the investors of securitised bonds. MIS improves the transparency, uniformity and judicious decision making. Decisions can be identified and ongoing improvements in the quality of service can be undertaken. The benefits of accessing new markets (investors of securitised bonds) generally overweigh the additional administrative burden.

3.15 Originator Discipline

The discipline that securitisation provides not only in the treasury area of the seller but throughout all other aspects of business has an increasingly positive influence on an FI. Both the demands of adhering to strict underwriting criteria and compliance with the asset servicing covenants provide the seller with the necessary incentives with which to manage its business. Securitisation encourages best practices.

3.16 Client Relationship effect

The sale of loans as securities while retaining the customer contact through loan servicing gives the Originator access to deposits and other customer service opportunities. Ownership of customer remains with the servicer by virtue of billing and collection procedures; only ownership of the financial instruments is transferred to the new investors. Thus the servicer benefits from customer relation without the obligation to keep his loan on the balance sheet.

3.17 Pooling

Similar debt instruments can be pooled to enhance creditworthiness and transform illiquid loans into liquid market securities. MBSs, automobiles, credit cards are the examples. In the case of life insurance, by pooling a large number of similar people, uncertainty of a single person’s default can be transformed into risk that can be priced, because objectively known probabilities can be attached to default. In the case of automobile loans, investors don't feel secure because they can repossess an automobile if a borrower defaults, but rather because, on average, these borrowers are unlikely to default beyond the level of credit enhancement. Automobile loans are marketable because investors can place a good bet on the pooled characteristics of people who borrow to purchase autos. Once they are pooled, auto loans have a demonstrably low risk of default (lower than the mandated capital requirement). As it is inefficient to hold them on bank's balance sheets, the market will find ways to release some bank capital that is tied up because of regulations that insure risk that the market does not perceive. Grouping of financial assets (loans etc.) into homogeneous pool facilitates actuarial analysis of risks. It also makes it easier for third parties such as credit rating agencies and credit enhancers to review and reinforce the credit underwriting decisions taken by the original lenders. However, this has limited application for commercial loans. The costs of evaluating the pool to ensure that you are not buying a bunch of lemons and, relatedly, the lack of agency rating make such instruments less suitable for securitisation.

3.18 Other benefits to FIs

There are no reserve requirements, either in the form of cash reserve or statutory liquidity ratios for cash generated through securitisation by FIs as Originators.

3.19 Benefits to Customers

Investors purchase risk-adjusted cash flow streams. This is accomplished through tranching of loan pool into multiple certificates based on relative levels of seniority and maturity. An auto loan or credit card receivables backed paper carries regular monthly cash flows, which can match, for example, the requirements of mutual funds for expected monthly redemption outflows. Investors who would like to invest for long term capital gain purposes may not like to be burdened with periodical interest receipt and the reinvestment risk thereof. Such investors can also bundled their interest instrument through securitisation process.

3.20 Overall benefits to the Originators and the financial system

Securitisation benefits the originators in the following ways:

 

 

 

 

 

 

 

 

  1. The use of capital can be optimised by reconfiguring portfolios to satisfy the risk-weighted capital adequacy norms better.
  2. Properly structured securitisation transactions enable Originators to focus on growth of their franchise without the need to focus on growth of capital base. Competitive advantage to Originators will be built on efficient marketing, tighter credit management, lower cost of servicing rather than be based on the ability to raise capital. Cost and capabilities amongst competitors are no longer muted; rather they are highlighted and magnified.
  3. Securitisation directly rewards better credit quality by reducing cost of credit enhancement and the costs of funds. This serves as a clear incentive for institutions to improve the quality of loan origination. In short, Originators who ensure better credit quality are rewarded by securitisation.
  4. Securitisation gives weaker firms a way out without a downward spiral effect. A case in point is the recent NBFC sector performance. The focus on limiting access on public deposits by NBFCs, by regulator and by rating agencies, has pushed even established NBFCs out of businesses that they have run successfully for many decades. If focus had been placed in helping these institutions securitise their assets, their financials would have improved and lesser risks would have been retained on their balance sheets.

Apart from the specific benefits to the Originator, the financial system as a whole also stands to benefit from securitisation in the following ways:

 

 

  • Securitisation breaks the process of lending and funding into several discrete steps leading to specialisation and economies of scale. This results in lower costs for the system as a whole and in the final analysis provides lower borrowing cost to the consumers. The most tangible result on account of the development of MBS market in United States is the reduction in the borrowing costs. MBS are priced at less than 100 basis points over similar tenure US Treasuryv. A financial market that has wide variety of options to issuers and investors, coupled with lower costs, has an inherent bias for growth.

 

 

  • The rate of asset turnover in the economy increases. For example, HFCs with excellent asset origination skills may have an insufficient balance sheet size to absorb the entire risk but can securitise loans in excess of what they feel comfortable with.

 

 

  • As a direct consequence of the above, the volume of resources available increases substantially. This assumes significance in light of the fact that our economy as a whole, and specific sectors such as housing and infrastructure in particular, are capital starved. For example, mortgage securitisation provides a breakaway from the "specialist circuit" of housing finance into a broader pool of resources. Further, securitisation facilitates flow of funds from capital surplus to capital deficient regions.

 

 

  • Along with flow of funds across regions, even risk is redistributed from high default to low default regions. Securitised instruments reach wider markets, provide more suitable instruments and remain more resilient to market cycles than conventional debt.

 

 

  • Component risks (credit, liquidity, interest rate, forex, and catastrophe) are segregated and distributed to market intermediaries equipped to absorb them most efficiently. This leads to a more stable financial system.

 

 

  • The debt market as a whole attains greater depth. This fact has been borne out by the experience in other countries. The capital markets can participate more directly in infrastructure/other long gestation projects.

Securitisation provides a higher leverage than refinance or directed credit. For example, an Rs 100 crore lending through refinance by NHB allows an Rs 100 crore lending of mortgage loans or at best Rs 200 crore by the HFI (which receives the refinance). If NHB were instead to provide a 10% or 5% credit enhancement, the HFI would be able to do Rs 1000 to Rs 2000 crore of mortgage lending. This multiplier effect is critically required to ensure flow of funds to many critical sectors such as infrastructure, housing, exports, etc.

Securitisation results in standardisation of industry practices since investors and rating agencies will increasingly start demanding 'conforming' assets in order to find an instrument 'investible'. This improves predictability of performance of portfolios and thus predictability in the financial performance of the Originator

Linkage to capital markets brings depth and dampens "stress". Greater flow of funds into various sectors, which securitisation can help cause, will result in more stable sector performance.

Conclusion - EMs

The most significant impact of securitisation arises from the placement of the different risks and rights of an asset with the most efficient owners. Securitisation provides capital relief, improves market allocation efficiency, improves the financial ratios of the FIs, can create a myriad of cash flows for the investors, suits risk profile of a variety of customers, enables the FIs to specialise in a particular activity, shifts the efficient frontier to the left, completes the markets with expanded opportunities for risk-sharing and risk-pooling, increases liquidity, facilitates asset-liability management, and develops best market practices. Securitisation is gaining acceptance as one of the fastest growing and most innovative forms of asset financing in today's world capital markets. Many companies in EMs have already used securitisation as part of their funding strategies. Some of the EM countries have, in fact, enacted a few legislations in quick succession to facilitate a better growth of securitisation market. The financial community, however, needs to be more aware of the benefits of the securitisation to help develop the market.

 


i Merton Robert, 'Financial Innovation and the Financial System', In: Cases in Financial Engineering, Mason, Merton, Perold, and Tufano (1995) p. 8.
ii Thibeault Andre E., Chairman, Nijenrode Centre for Finance, Nijenrode University, Reader for the course 'Management of Financial Institutions' (1997-98), p. 114
5 Emery, Douglas R.; Finnerty, John D. Corporate Financial Management. New Jersey : Prentice Hall, 1997, p.482.
6Rosenthal James A. ; Ocampo Juan M, 'Securitisation of Credit', NewYork John Wiley & Sons, Inc. p. 14
iii Allen, Craig M. and Thomas Annie, Aegis Financial Advisors, Inc., NewYork In. 'Securitisation of assets: a corporate strategy and its implications'iv The Economic Times, June 24, 1998
v Citi Bank Mumbai

 

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Annex 2: Methodologies

2.1 Scheduled Commercial Banks

(a) Banking stability indicator (BSI) and map

The banking stability map and indicator present an overall assessment of changes in underlying conditions and risk factors that have a bearing on the stability of the banking sector during a period. The six composite indices represent risk in six dimensions - soundness, asset quality, profitability, liquidity, efficiency and sensitivity to market risk. Each composite index is a relative measure of risk during the sample period used for its construction, where a higher value would mean higher risk in that dimension.

The financial ratios used for constructing each composite index are given in Table 1. Each financial ratio is first normalised for the sample period using the following formula:

where Xt is the value of the ratio at time t. If a variable is negatively related to risk, then normalisation is done using 1-Yt. Composite index of each dimension is then calculated as a simple average of the normalised ratios in that dimension. Finally, the banking stability indicator is constructed as a simple average of these six composite indices. Thus, each composite index and the overall banking stability indicator takes values between zero and one.

Table 1: Ratios used for constructing the Banking Stability Indicator and Map
Dimension Ratios
Soundness CRAR # Net NPAs to Capital Tier 1 Capital to Assets #  
Asset Quality Gross NPAs to Total Advances Provisioning Coverage Ratio # SMA-1 and SMA-2 Loans to Total Advances Restructured Standard Advances to Standard Advances
Profitability Return on Assets # Net Interest Margin # Growth in Earnings Before Provisions and Taxes # Interest Margin to Gross Income #
Liquidity Liquid Assets to Total Assets # Liquidity Coverage Ratio # Non-Bank Advances to Customer-Deposits  
Efficiency Cost to Income Business (Credit + Deposits) to Staff Expenses # Staff Expenses to Operating Expenses  
Sensitivity to market risk RWA (market risk) to Capital PV01 of HFT and AFS Investments to Total Capital Total Net Open Position in Forex to Total Capital  
Note: # Negatively related to risk.

(b) Macro stress test

Macro stress test evaluates the resilience of banks against adverse macroeconomic shocks. It attempts to assess the impact on capital ratios of banks1 over a one-and-half to two-year horizon, under a baseline and two adverse scenarios. The test encompasses credit risk, market risk and interest rate risk in the banking book. The salient features are as below:

I. Macro-scenario design: The test envisages three scenarios - a baseline and two hypothetical adverse macro scenarios. While the baseline scenario is derived from the forecasted path of select macroeconomic variables, the two adverse scenarios are derived based on hypothetical stringent stress scenario narratives and by performing simulations using the following Vector Autoregression with Exogenous Variables (VARX) model,

with GDP growth, CPI inflation, repo rate and lending spread as the endogenous variables and US GDP growth and US-VIX as exogeneous variables.

II. Projection of key financial variables: Slippage ratio, interest income and interest expense are projected at bank-level using panel regression models for each bank group. GNPA ratio and provision are projected using structural models. Non-interest income [comprising of (a) fee income and (b) other operating income excluding fee income] and non-interest expense are projected based on assumed growth rate of these variables under each scenario.

(i) Projection of slippage ratio: The quarterly slippage ratios at bank level are projected using the following panel regression model;

Zi,t is the quarterly slippage ratio of bank i during quarter t, Xt is a vector of macroeconomic variables including lending spread and GDP growth, μ'i represents bank-specific fixed effects, λ'it represents adjustments for specific quarters and ε'i,t is an i.i.d. error term. Subsequently, quarterly slippage ratios, i,t s are computed based on first differences of the regression equation (2) as,

(ii) Projection of gross loans and advances: Bank level gross loans and advances are projected by applying growth rate equivalent to nominal GDP growth as,

where Li,t represents the gross loans and advances of bank i at the end of quarter t, and gt represents the nominal GDP growth rate during quarter (t-1, t).

(iii) Projection of non-performing loans (NPL) or GNPAs: Bank-level GNPAs are projected using the equation,

where NPLi,t represents the stock of GNPA of bank i at the end of quarter t, WROi,t, CURERi,t and RECRi,t are write-off, upgradation and recovery rates of bank i during the quarter t respectively, PDi,t is the probability of default (slippage ratio) projected in (3) and PLi,t-1 is the stock of performing loans at the end of quarter t-1.

(iv) Projection of performing loans (PL): The stock of performing loans for bank i at the end of quarter t, PLi,t is projected as,

(v) Projection of provisions: Provisions of bank i for quarter t are projected as follows,

where provisioning coverage ratio (PCR) is assumed at 75 per cent. The loss given default (LGD) during quarter t is derived based on the model of Frye and Jacobs (2012), as below

and the parameter k is derived as,

PD* and LGD* are long-term average PDs and LGDs and Φ represents the cumulative normal distribution function.

(vi) Projection of interest income and expenses: Interest income (as share of interest-earning assets) and interest expenses (as share of interest-bearing liabilities) are modelled as functions of macroeconomic variables (GDP growth and call rate) and bank fixed effects with structure similar to equation (2). Bank-wise projections of these ratios are applied to derive shocks to yield on assets and cost of funds for each bank.

(vii) Projection of market risk: Market risk is estimated by applying MTM revaluation of bond exposures (AFS and HFT portfolio) of banks using three inputs, (i) bond exposure, (ii) Macaulay duration, and (iii) interest rate shock, using the bond revaluation formula:

where D is the Macaulay duration, r is the risk-free rate, s is credit spread component, t is the time steps until maturity T, V is the market value, Δrt+1 represents the risk-free rate shift and Δst+1 the credit spread shift. Further, equity and foreign exchange risk are also factored into market risk.

(viii) Projection of net profit: Net profit is projected as,

(ix) Projection of capital: Capital is projected as,

(x) Projection of risk weighted assets (RWA): RWA for Credit risk is projected as,

where gt represents the nominal GDP growth rate during the period (t, t+1).

RWA for market risk and RWA for operational risk are also projected to grow at nominal GDP growth rate.

III. Major assumptions: Provisions for income tax are assumed at 30 per cent, 30 per cent and 35 per cent of profit before tax for public sector banks (PSBs), private sector banks (PVBs) and foreign banks (FBs), respectively. Dividend payout ratio is assumed at 35 per cent of net profit. Balance sheet is projected to grow at the rate of nominal GDP growth.

(c) Single factor sensitivity analysis – Stress testing

As part of quarterly surveillance, stress tests are conducted covering credit risk, interest rate risk, liquidity risk, equity price risk. and the resilience of scheduled commercial banks (SCBs) in response to these shocks is studied. The analysis is done on individual SCBs as well as on the system level.

I. Credit risk (includes concentration risk)

To ascertain the resilience of banks, the credit portfolio was given a shock by increasing GNPA ratio for the entire portfolio. For testing the credit concentration risk, default of the top individual borrower(s) and the largest group borrower(s), in terms of credit outstanding, was assumed. The analysis was carried out both at the aggregate level as well as at the individual bank level. In case of credit risk, the assumed increase in GNPAs was distributed across sub-standard, doubtful and loss categories in the same proportion as prevailing in the existing stock of GNPAs at system level. However, for credit concentration risk (exposure based), the additional GNPAs under the assumed shocks were considered to fall into sub-standard category only and for credit concentration risk (stressed advances based), stressed advances were considered to fall into loss category. The provisioning requirements were taken as 25 per cent, 75 per cent and 100 per cent for sub-standard, doubtful and loss advances, respectively. These norms were applied on additional GNPAs calculated under a stress scenario. As a result of the assumed increase in GNPAs, loss of income on the additional GNPAs for one quarter was also included in total losses, in addition to the incremental provisioning requirements. The estimated provisioning requirements so derived were deducted from banks’ capital and the capital adequacy ratios under stress scenarios were computed.

II. Sectoral credit risk

To ascertain the sectoral credit risk of individual banks, the credit portfolios of a particular sector was given a shock by increasing GNPA ratio for the sector, based on standard deviation (SD) of GNPA ratios of the sector. The additional GNPAs under the assumed shocks were considered to fall into sub-standard category only. Calculation of the impact on capital is similar to that of stress test for credit risk described above.

III. Interest rate risk

Under assumed shocks of shift in the INR yield curve, there could be losses on account of the fall in value of the portfolio or decline in income.

For interest rate risk in the investment portfolio: AFS, FVTPL (including HFT book) and HTM categories, a duration analysis approach was considered for computing the valuation impact (portfolio losses). The portfolio losses on these investments were calculated for each time bucket of AFS, FVTPL (including HFT book) and HTM categories based on the applied shocks. These estimated losses were reduced from banks’ capital and market risk weighted losses from RWA to arrive at capital ratios under stress scenarios.

Interest rate risk of banks refers to the risk to a bank’s capital and earnings arising from adverse movements in interest rates that affect bank’s books. The impact on earnings is measured using the traditional gap analysis (TGA) and the capital impact is measured by duration gap analysis (DGA). The focus of TGA is to measure the level of a bank’s exposure to interest rate risk in terms of the sensitivity of its net interest income (NII) to interest rate movements over one-year horizon. It involves bucketing of all rate-sensitive assets (RSA), rate-sensitive liabilities (RSL), and off-balance sheet items as per residual maturity / re-pricing date, in various time bands and computing earnings-at-risk (EAR) i.e., loss of income under different interest rate scenarios over a time horizon of one year. Advances, investments, swaps / forex swaps and reverse repos are the major contributors to RSA whereas deposits, swaps / forex swaps and repos are the main elements under RSL. The DGA involves bucketing of all RSA and RSL as per residual maturity / re-pricing dates in various time bands and computing the modified duration gap (MDG) to estimate the impact on the market value of equity. MDG is calculated with the following formula: MDG = [MDA - MDL * (RSL / RSA)], where MDA and MDL are the weighted averages of the modified duration (MD) of items of RSA and RSL, respectively. Thereafter, change in market value of equity (MVE) is computed as ΔE/ E = -[MDG]*RSA* Δi/ E, where Δi is the change in interest rate and E is equity (i.e. net worth).

IV. Equity price risk

Under the equity price risk, the impact of the shock of a fall in the equity price index, by certain percentage points, on bank capital was examined. The loss due to the fall in the value of the portfolio on account of change in equity prices is deducted from the bank’s capital to arrive at the capital under stress scenarios.

V. Liquidity risk

Liquidity stress test assesses the ability of a bank to withstand unexpected liquidity drain without taking recourse to any outside liquidity support. The stress test is based on the Liquidity Coverage Ratio (LCR) framework. The baseline scenario for the stress test depicts the extant LCR computation guidelines and accordingly applies weights used for LCR computation, to each component of cash outflows, inflows and liquid assets. The adverse stress scenarios are designed by applying higher run-off rates relative to the baseline scenario to certain cash outflows (Table 2). LCR for each bank is computed under each of these scenarios.

Table 2: Run-off Factors applied on Cash Outflow Components
(in per cent)
Scenarios Baseline Stress Scenario 1 Stress Scenario 2
Retail Deposits      
Stable deposits 5 6 7
Less stable retail deposits 10 11 12
Unsecured Wholesale Funding      
Demand and term deposits, residual maturity < 30 days, small business      
Stable deposits 5 6 7
Less stable deposits 10 11 12
Nonfinancial corporates, sovereigns, central banks, multilateral development banks, PSEs 40 42.5 45
Currently undrawn but committed Credit and Liquidity Facilities      
Retail and small business 5 10 12
Nonfinancial corporates, sovereigns, central banks, multilateral development banks, PSEs      
Credit facilities 10 12 15
Liquidity facilities 30 40 50

(d) Bottom-up stress testing: Credit, market and liquidity risks

Bottom-up sensitivity analyses for credit, market and liquidity risks were performed by 37 select scheduled commercial banks. A set of common stress scenarios and shocks were provided to the select banks. The tests were conducted by the banks using relevant data at end-March 2025 and their own methodologies for calculating losses in each case.

(e) Bottom-up stress testing: Derivatives portfolios of select banks

Stress tests on derivatives portfolio (in terms of notional value) were carried out by a sample of 36 banks, constituting the major active authorised dealers and interest rate swap counterparties. Each bank in the sample was asked to assess the impact of stress conditions on their respective derivatives portfolio.

In case of domestic banks, the derivatives portfolio of both domestic and overseas operations was included. In case of foreign banks, only the domestic (Indian) position was considered for the exercise. Derivatives trades where hedge effectiveness was established were exempted from the stress tests, while all other trades were included.

The stress scenarios incorporated four shocks consisting of the spot USD-INR rate and domestic interest rates as parameters (Table 3).

Table 3: Shocks for sensitivity analysis
  Domestic interest rates
Shock 1 Overnight +2.5 percentage points
Up to 1-year +1.5 percentage points
Above 1-year +1.0 percentage points
  Domestic interest rates
Shock 2 Overnight -2.5 percentage points
Up to 1-year -1.5 percentage points
Above 1-year -1.0 percentage points
  Exchange rates
Shock 3 USD-INR +20 per cent
  Exchange rates
Shock 4 USD-INR -20 per cent

2.2 Primary (Urban) Co-operative Banks

Single factor sensitivity analysis – Stress testing

Stress testing of UCBs was conducted with reference to the reported position as of March 2025. The banks were subjected to baseline, medium and severe stress scenarios in the areas of credit risk, market risk and liquidity risk as follows:

I. Credit default risk

  • Under credit default risk, the model aims to assess the impact of stressed credit portfolio of a bank on its CRAR.

  • The arithmetic mean of annual growth rate of GNPAs was calculated separately for each NPA class (sub-standard, doubtful 1 (D1), doubtful 2 (D2), doubtful 3 (D3) and loss assets) based on reported data between 2009 and 2024 for the UCB sector as a whole. This arithmetic mean of annual growth rate formed the baseline stress scenario, which was further stressed by applying shocks of 1.5 standard deviation (SD) and 2.5 SD to generate medium and severe stress scenarios for each category separately. These were further adjusted based on NPA divergence level.

  • Based on the above methodology, the annual NPA growth rate matrix arrived at under the three scenarios are as below.

(per cent)
  Increase in Substandard Assets Increase in D1 assets Increase in D2 assets Increase in D3 assets Increase in Loss assets
Baseline 21.71 17.10 15.93 14.38 29.83
Medium Stress 62.37 46.09 39.56 49.27 169.57
Severe Stress 89.47 65.42 55.32 72.53 262.72

II. Credit concentration risk

  • The impact of CRAR, under assumed scenarios of top 1, 2, 3 single borrower exposures moving to ‘loss advances’ category, requiring 100 per cent provisioning, was assessed. These exposures may not necessarily be ‘standard advances’ but are identified based on their potential to require higher provisioning, thereby reflecting more impactful stress scenario.

III. Interest rate risk in trading book

  • Duration analysis approach was adopted for analysing the impact of upward movement of interest rates on the AFS and HFT portfolio of UCBs.

  • Upward movement of interest rates by 50 bps, 100 bps and 150 bps were assumed under the three stress scenarios and consequent provisioning impact on CRAR was assessed.

IV. Interest rate risk in banking book

  • The banking book of UCBs was subjected to interest rate shocks of 50 bps, 100 bps and 150 bps under three stress scenarios and its impact on net interest income was assessed.

V. Liquidity risk

  • The stress test was conducted based on cumulative cash flows in the 1-28 days’ time bucket. The cash inflows and outflows were stressed under baseline, medium, and severe scenarios.

  • While the inflows are stressed uniformly at 5 per cent under all the stress scenarios, outflows are stressed based on worst negative deposit growth recorded across quarters for the periods ranging across past ten years (2014 - 2024). Since UCBs are primarily dependent on deposits as major source of funds, negative growth in deposits is considered as representative of stressed outflows. Further, three months period is considered as representative of 1-28 days’ bucket as this is the closest short-term period for which deposits data is available for all the banks (given that all the banks submit quarterly returns). The average of worst negative deposit growth rate for ten years is considered as baseline scenario, which is further stressed by 1.5 SD and 2.5 SD to generate medium and severe stress scenarios for outflows.

  • The banks with negative cumulative mismatch (cash inflow less cash outflow) exceeding 20 per cent of the outflows were considered to be under stress on the basis of the circular RBI/2008-09/174 UBD. PCB. Cir. No12/12.05.001/2008-09 dated September 17, 2008, which stipulates that the mismatches (negative gap between cash inflows and outflows) during 1-14 days and 15-28 days’ time bands in the normal course should not exceed 20 per cent of the cash outflows in each time band.

2.3 Non-Banking Financial Companies (NBFCs)

(a) Non-banking stability indicator (NBSI) and map

The non-banking financial company (NBFC) stability indicator (NBSI) presents an overall assessment of changes in underlying conditions and risk factors that have a bearing on the stability of the NBFC sector during a period. In line with the scale-based regulatory structure, NBFCs falling in the upper and middle layers (excluding the Core Investment Companies (CICs), Primary Dealers (PDs) and Housing Finance Companies (HFCs)) have been considered for construction of the indicator and a related stability map.

The NBSI constitutes five composite indices representing risks in five dimensions – soundness, asset-quality, profitability, liquidity and efficiency. Each composite index is a relative measure of risk and is constructed using multiple financial ratios in respective risk dimension (Table 4). A higher value of a composite index would mean higher risk in that dimension.

Each financial ratio is first normalized for the sample period using the following formula:

where Xt is the value of the financial ratio at time t. If a variable is negatively related to risk, then it is normalized using 1-Yt. Composite index of each dimension is then calculated as a simple average of the normalized ratios in that dimension. Finally, the NBSI is constructed as a simple average of these five composite indices. Each composite index and the overall NBSI take values between zero and one.

Table 4: Ratios used for constructing the Non-Banking Stability Indicator and Map
Dimension
Soundness CRAR # Net NPAs to Capital Tier 1 Capital to Assets #
Asset Quality Gross NPAs to Total Advances Provisioning Coverage Ratio # Sub-Standard Advances to Gross NPAs#
Profitability Return on Assets # Net Interest Margin # Return on Net Owned Funds #
Liquidity Short-term Liability to Total Assets Long-term Assets to Total Assets Dynamic Liquidity#
Efficiency Cost to Income Staff Expense to Total Expense Business to Staff Expense#
Note: # Negatively related to risk.

(b) Single factor sensitivity analysis - Stress testing

Credit and liquidity risk stress tests for NBFCs have been performed under baseline, medium and high risk scenarios.

I. Credit risk

Major items of the balance sheet of NBFCs over one year horizon were projected by applying moving average and smoothing techniques. Assets, advances to total assets ratio, earnings before profit and tax (EBPT) to total assets ratio, risk-weight density and slippage ratio were projected over the next one year; and thereafter, based on these projections – new slippages, provisions, EBPT, risk-weighted assets and capital were calculated for the baseline scenario. For the medium and high-risk scenarios, GNPA ratios under baseline scenario were increased by 1 SD and 2 SD and accordingly revised capital and CRAR were calculated.

II. Liquidity risk

Cash flows under stress scenario and mismatch in liquidity position were calculated by assigning assumed percentage of stress to the overall cash inflows and outflows in different time buckets over the next one year. Projected outflows and inflows over the next one year were considered for calculating the liquidity mismatch under the baseline scenario. Outflows and inflows of the sample NBFCs were applied a shock of 5 per cent and 10 per cent for time buckets over the next one year for the medium and high-risk scenarios, respectively. Cumulative liquidity mismatch due to such shocks were calculated as per cent of cumulative outflows and, NBFCs with negative cumulative mismatch were identified.

2.4 Stress Testing Methodology of Mutual Funds

The SEBI has mandated all open-ended debt schemes (except overnight schemes) to conduct stress testing. Accordingly, Association of Mutual Funds in India (AMFI) prescribed the “Best Practice Guidelines on Stress Testing by Debt Schemes of Mutual Funds”. The stress testing is carried out internally by all Asset Management Companies (AMCs) on a monthly basis and also when the market conditions require so. A uniform methodology is being followed across the industry for stress testing with a common outcome, i.e., impact on NAV as a result of the stress testing.

Stress testing parameters

The stress testing is conducted on the three risk parameters, viz., interest rate risk, credit risk and liquidity risk.

(a) Interest rate risk parameter

For interest rate risk parameter, AMCs subject the schemes at portfolio level to the following scenarios of interest rate movements and assess the impact on NAV.

  1. The highest increase in G-Sec yield in the last 120 months (1-year G-Secs or 10-year G-Secs whichever is higher on month-on-month basis comparing maximum yield of a month to minimum yield of previous month).

  2. Two-third of the highest increase in G-Sec yield in the last 120 months.

  3. One-third of the highest increase in G-Sec yield in the last 120 months

(b) Credit risk parameter

For credit risk parameter, AMCs may subject the securities held by the scheme to the following:

  1. Calculate the probability of downgrade of each security. In this regard, to incorporate all possible downgrade scenarios (notches) for each security, probability tables published by rating agencies are being used.

  2. Further, each potential notched down rating will correspond to a change in valuation yield for the security corresponding to that change in rating. The change in valuation yields for the respective rating changes is derived from the valuation matrix used by the valuation agencies.

  3. The sum product of probability of downgrade within investment grade and change in yield on that downgrade of a security, is then multiplied by the duration of that security and the weightage of that security in the portfolio. Separately, the sum product of probability of downgrade below investment grade with haircut applicable on that downgrade of any security, is multiplied with the weightage of that security in the portfolio. These two sum products are added to get the aggregate potential impact at a security level.

  4. The summation of all these security level outputs is considered as the portfolio level credit impact.

(c) Liquidity risk parameter

For liquidity risk parameter, the following analysis is being undertaken:

  1. Data for past periods of stress (viz. stress scenarios during the years 2008, 2013, 2018, 2020) along with rise in yields for a given credit rating, type of security, etc. in respective matrices for the relevant duration bucket is considered.

  2. The change in median yield differential over G-Sec during stress period compared to the preceding normal period (normal period is a period starting 6 months prior to the start of the stress period and ending at the start of the stress period) is considered as rise in spread for the purpose of stress testing.

  3. AMCs take yield spike as higher than the AMFI-specified values for stress testing based on market scenarios.

  4. These calculations are again reiterated for individual securities based on respective ratings, matrix-based sector as provided in the matrix files and duration bucket and aggregated at the portfolio level to get the portfolio level output.

AMCs additionally consider extreme stress scenarios of time bound liquidation (viz 5 days, 3 days and 1 day) of full portfolios and its impact on NAV by applying suitable haircuts.

2.5 Methodology for Stress Testing Analysis at Clearing Corporations

The SEBI has specified the granular norms related to core settlement guarantee fund (SGF); stress testing and default procedures to create a core fund (called core SGF) within the SGF against which no exposure is given and which is readily and unconditionally available to meet settlement obligations of clearing corporation in case of clearing member(s) failing to honour settlement obligation; align stress testing practices of clearing corporations with Principles for Financial Market Infrastructures (norms for stress testing for credit risk, stress testing for liquidity risk and reverse stress testing including frequency and scenarios); capture the risk due to possible default in institutional trades in stress testing; harmonise default waterfalls across clearing corporations; limit the liability of non-defaulting members in view of the Basel capital adequacy requirements for exposure towards central counterparties (CCPs); ring-fence each segment of clearing corporation from defaults in other segments; and bring in uniformity in the stress testing and the risk management practices of different clearing corporations especially with regard to the default of members.

Stress testing is carried out at clearing corporations (CCs) to determine the minimum required corpus (MRC), which needs to be contributed by clearing members (CMs) to the core SGF. The MRC is determined separately for each segment (viz. cash market, equity derivatives, currency derivatives, commodity derivatives, debt and tri-party repo segment) every month based on stress testing subject to the following:

  1. The MRC is fixed for a month.

  2. By 15th of every month, CCs review and determine the MRC for next month based on the results of daily stress tests of the preceding month.

  3. For every day of the preceding month, uncovered loss numbers for each segment are estimated based on stress test and highest of such numbers is taken as worst-case loss number for the day.

  4. Average of all the daily worst case loss numbers determined in (iii) above is calculated.

  5. The MRC for next month is at least the higher of the average arrived in at step (iv) above and the segment MRC as per previous review.

For determining the MRC for cash, equity derivatives and currency derivatives segment, CCs calculate the credit exposure arising out of a presumed simultaneous default of top two CMs. The credit exposure for each CM is determined by assessing the close-out loss arising out of closing open positions (under stress testing scenarios) and the net pay-in/ pay-out requirement of the CM against the required margins and other mandatory deposits of the CM. The MRC or average stress test loss of the month is determined as the average of all daily worst case loss scenarios of the month. The actual MRC for any given month is determined as at least the higher of the average stress test loss of the month or the MRC arrived at any time in the past. For the debt segment, the trading volume is minimal, and hence the MRC for the core SGF is calculated as higher of ₹4 crore or aggregate losses of top two CMs, assuming close out of obligations at a loss of four per cent less required margins. The tri-party repo segment and commodity derivatives segment also follow the same stress testing guiding principles as prescribed for equity cash, equity derivatives and currency derivatives segments. For commodity derivatives segment, however, MRC is computed as the maximum of either credit exposure on account of the default of top two CMs or 50 per cent of credit exposure due to simultaneous default of all CMs. Further, the minimum threshold value of MRC for commodity derivatives segment of any stock exchange is ₹10 crore.

CCs carry out daily stress testing for credit risk using at least the standardized stress testing methodology prescribed by SEBI for each segment. Apart from the stress scenarios prescribed for cash market and derivatives market segments, CCs also develop their own scenarios for a variety of ‘extreme but plausible market conditions’ (in terms of both defaulters’ positions and possible price changes in liquidation periods, including the risk that liquidating such positions could have an impact on the market) and carry out stress testing using self-developed scenarios. Such scenarios include relevant peak historic price volatilities, shifts in other market factors such as price determinants and yield curves, multiple defaults over various time horizons and a spectrum of forward-looking stress scenarios in a variety of extreme but plausible market conditions. Also, for products for which specific stress testing methodology has not been prescribed, CCs develop extreme but plausible market scenarios (both hypothetical and historical) and carry out stress tests based on such scenarios and enhance the corpus of SGF, as required by the results of such stress tests.

2.6 Interconnectedness – Network Analysis

Matrix algebra is at the core of the network analysis, which uses the bilateral exposures between entities in the financial sector. Each institution’s lending to and borrowings from all other institutions in the system are plotted in a square matrix and are then mapped in a network graph. The network model uses various statistical measures to gauge the level of interconnectedness in the system. Some of the important measures are given below:

ii) Cluster coefficient: Clustering in networks measures how interconnected each node is. Specifically, there should be an increased probability that two of a node’s neighbours (banks’ counterparties in case of a financial network) are neighbours to each other also. A high clustering coefficient for the network corresponds with high local interconnectedness prevailing in the system. For each bank with ki neighbours the total number of all possible directed links between them is given by ki(ki-1). Let Ei denote the actual number of links between bank i’s ki neighbours. The clustering coefficient Ci for bank i is given by the identity:

The clustering coefficient (C) of the network as a whole is the average of all Ci’s:

iii) Tiered network structures: Typically, financial networks tend to exhibit a tiered structure. A tiered structure is one where different institutions have different degrees or levels of connectivity with others in the network. In the present analysis, the most connected banks are in the innermost core. Banks are then placed in the mid-core, outer core and the periphery (the respective concentric circles around the centre in the diagram), based on their level of relative connectivity. The range of connectivity of the banks is defined as a ratio of each bank’s in-degree and out-degree divided by that of the most connected bank. Banks that are ranked in the top 10 percentile of this ratio constitute the inner core. This is followed by a mid-core of banks ranked between 90 and 70 percentile and a 3rd tier of banks ranked between the 40 and 70 percentile. Banks with a connectivity ratio of less than 40 per cent are categorised in the periphery.

iv) Colour code of the network chart: The blue balls and the red balls represent net lender and net borrower banks respectively in the network chart. The colour coding of the links in the tiered network diagram represents the borrowing from different tiers in the network (for example, the green links represent borrowings from the banks in the inner core).

(a) Solvency contagion analysis

The contagion analysis is in the nature of a stress test where the gross loss to the banking system owing to a domino effect of one or more banks failing is ascertained. We follow the round by round or sequential algorithm for simulating contagion that is now well known from Furfine (2003). Starting with a trigger bank i that fails at time 0, we denote the set of banks that go into distress at each round or iteration by Dq, q = 1,2, …For this analysis, a bank is considered to be in distress when its Tier I capital ratio goes below 7 per cent. The net receivables have been considered as loss for the receiving bank.

(b) Liquidity contagion analysis

While the solvency contagion analysis assesses potential loss to the system owing to failure of a net borrower, liquidity contagion estimates potential loss to the system due to the failure of a net lender. The analysis is conducted on gross exposures between banks comprising both fund based ones and derivatives. The basic assumption for the analysis is that a bank will initially dip into its liquidity reserves or buffers to tide over a liquidity stress caused by the failure of a large net lender. The items considered under liquidity reserves are: (a) excess CRR balance; (b) excess SLR balance; and (c) 18 per cent of NDTL. If a bank is able to meet the stress with liquidity buffers alone, then there is no further contagion.

However, if the liquidity buffers alone are not sufficient, then a bank will call in all loans that are ‘callable’, resulting in a contagion. For the analysis only short-term assets like money lent in the call market and other very short-term loans are taken as callable. Following this, a bank may survive or may be liquidated. In this case there might be instances where a bank may survive by calling in loans, but in turn might propagate a further contagion causing other banks to come under duress. The second assumption used is that when a bank is liquidated, the funds lent by the bank are called in on a gross basis (referred to as primary liquidation), whereas when a bank calls in a short-term loan without being liquidated, the loan is called in on a net basis (on the assumption that the counterparty is likely to first reduce its short-term lending against the same counterparty. This is referred to as secondary liquidation).

(c) Joint solvency-liquidity contagion analysis

A bank typically has both positive net lending positions against some banks while against some other banks it might have a negative net lending position. In the event of failure of such a bank, both solvency and liquidity contagion will happen concurrently. This mechanism is explained by the following flowchart:

Flowchart of Joint Liquidity-Solvency contagion due to a bank coming under distress

The trigger bank is assumed to have failed for some endogenous reason, i.e., it becomes insolvent and thus impacts all its creditor banks. At the same time it starts to liquidate its assets to meet as much of its obligations as possible. This process of liquidation generates a liquidity contagion as the trigger bank starts to call back its loans.

Since equity and long-term loans may not crystallise in the form of liquidity outflows for the counterparties of failed entities, they are not considered as callable in case of primary liquidation. Also, as the RBI guideline dated March 30, 2021 permits the bilateral netting of the MTM values in case of derivatives at counterparty level, exposures pertaining to derivative markets are considered to be callable on net basis in case of primary liquidation.

The lender / creditor banks that are well capitalised will survive the shock and will generate no further contagion. On the other hand, those lender banks whose capital falls below the threshold will trigger a fresh contagion. Similarly, the borrowers whose liquidity buffers are sufficient will be able to tide over the stress without causing further contagion. But some banks may be able to address the liquidity stress only by calling in short term assets. This process of calling in short term assets will again propagate a contagion.

The contagion from both the solvency and liquidity side will stop / stabilise when the loss / shocks are fully absorbed by the system with no further failures.

(d) Identification of impactful and vulnerable banks

Data on bilateral exposures among entities of the financial system are leveraged to compute impact and vulnerability metrics to identify entities that are impactful (causing sizeable capital loss to others in the system upon their default) as well as vulnerable (their own capital loss susceptibility conditional on other entities’ failures), using the following metrics and methodology (IMF, 2017):

(i) Index of contagion (impact) of a bank represents the average loss experienced by other banks (expressed as a percentage of their Tier 1 capital) due to failure of that bank. It is calculated, for bank i, as

where Kj is bank j’s capital, Lji is the loss to bank j due to the default of bank i and N is the total number of banks;

(ii) Index of vulnerability of a bank represents the average loss experienced by the bank (expressed as a percentage of its Tier 1 capital) across individually triggered failures of all other banks. It is calculated, for bank i, as

where Ki is bank i’s capital, Lij is the loss to bank i due to the default of bank j and N is the total number of banks;

(iii) To analyse the effects of a credit shock, the exercise simulates default of each bank with 100 per cent loss-given-default, where the counterparties’ capitals absorb the losses. A bank is said to fail if its Tier 1 capital ratio falls below 7 per cent. In the subsequent rounds, if there are further failures, the losses are aggregated.

The results of indexes calculated can be analysed to identify entities that are common between the set of top highly impactful banks and the set of top highly vulnerable banks.

2.7 Financial System Stress Indicator (FSSI)

FSSI is compiled using risk factors spread across five financial market segments (equity, forex, money, government debt and corporate debt), three financial intermediary segments (banks, NBFCs and AMC-MFs) and the real sector (Table 5). FSSI lies between zero and unity, with higher value indicating more stress. For its construction, the risk factors pertaining to each component segment are first normalised using min-max method and thereafter aggregated based on simple average into a sub-indicator ‘yi‘ representing the ith market / sector. Finally, the composite FSSI is obtained as,

where the weight ‘wi’ of each sub-indicator ‘yi’ is determined from its sample standard deviation ‘si’, as,

 
Table 5: Risk factors constituting each component of FSSI
Equity Market 1. Difference between NIFTY 50 monthly returns and its maximum over a two-year rolling window
2. NIFTY 50 Market capitalisation-to-GDP ratio
3. NSE-VIX Index
4. Net Equity FPI flows
Government Debt Market 5. Realised volatility in 10-year G-sec yield
6. Term Spread: Spread between 10-year G-sec yield and 3-month T-Bill rate
7. Increase in the 10-year G-sec yield compared to the minimum over a two-year rolling window
8. Net Debt FPI flows
Forex Market 9. Difference between rupee dollar exchange rate and its maximum over a two-year rolling window.
10. m-o-m appreciation/depreciation of rupee dollar exchange rate
11. GARCH (1,1) volatility of rupee dollar exchange rate
12. Difference between 3-month forward premia and its historical maximum.
Money/Short Term Market 13. Spread between weighted average call rate and weighted average market repo rate
14. Spread between 3-month CD rate and 3-month T-Bill rate
15. Spread between 3-month non-NBFC CP rate and 3-month T-Bill rate
16. Realised volatility of 3-month CP rate
17. Spread between 3-month OIS rate and 3-month T-Bill rate
Corporate Bond Market 18. Yield spread between 3-year AAA corporate bonds and 3-year G-sec
19. Difference between 3-year BBB and 3-year AAA corporate bond yield
20. Difference between 3-year BBB corporate bond yield and its maximum
Banking Sector SCBs 21. CRAR (SCBs)
22. RoA (SCBs)
23. LCR (SCBs)
24. Cost-to-Income (SCBs)
25. Stressed Assets Ratio (SCBs)
26. Banking Beta: cov(r,m)/var(m), over 2-year moving window.
r= Bank NIFTY y-o-y, m= NIFTY 50 y-o-y
UCBs 27. GNPA ratio (UCBs)
28. CRAR (UCBs)
29. RoA (UCBs)
NBFC Sector 30. GNPA ratio 31. CRAR
32. RoA
33. Spread between 3-month NBFC CP rate and 3-month T-Bill rate
AMC-MF Sector 34. Mutual fund redemptions: y-o-y
35. Mutual fund net inflows
Real Sector 36. GDP growth
37. CPI inflation
38. Current account balance as a share of GDP
39. Gross fiscal deficit as a share of GDP

1 The macro stress test is carried out on select 46 scheduled commercial banks (SCBs).

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Annex 1: Systemic Risk Survey

In the latest round of the Systemic Risk Survey, all the major risk groups were perceived to be in the medium-risk category. Risk perception of global and institutional risks increased marginally, while macroeconomic and financial market risks have moderated due to benign inflation and monetary easing. Overall, the survey respondents viewed geopolitical conflicts, capital outflows and reciprocal tariff/ trade slowdown as major near-term potential risks to financial stability.

The 28th round of the Reserve Bank’s Systemic Risk Survey (SRS) was conducted in May 2025 to gauge the perceptions of experts, including economists and market participants, on the major vulnerabilities of the Indian financial system. Considering prevailing macroeconomic and financial conditions, the current round of the survey, in addition to regular questions, also captures the respondents’ views on (i) impact of trade tension and protectionist policies on overall financial stability, (ii) effect of trade slowdown on banking sector and (iii) the major sectors affected by global trade disruptions.

A summary of feedback from 50 respondents is presented below.

  • All the major risk groups were perceived to be in the medium-risk category. Risk perception of global and institutional risks increased marginally, primarily on account of global growth concerns, geopolitical conflicts, profitability risk and cyber risk. On the other hand, macroeconomic and financial market risks have moderated due to benign inflation and monetary easing (Figure 1).

Figure 1: Systemic Risk Survey: Major Risk Groups
  • In the global risks, geopolitical conflicts/ geo-economic fragmentation scored the highest (that is, worst risk assessment) compared with other risk sub-categories. Global growth risk moved from the medium to the high-risk category in the latest survey, signalling increasing growth pessimism among the panellists. The funding risk (impact on external borrowings) increased marginally within the medium-risk category.

  • In terms of macroeconomic risks, the risk of investment growth and climate-related risk have increased, whereas inflation risk has decreased significantly and moved from medium to low-risk categories. Other risks such as domestic growth, current account deficits, and household savings remained unchanged.

  • Among the financial market risks, the risk perception of all individual risk categories declined. Equity price volatility continued to remain in the high-risk category.

  • In the case of institutional risks, cyber risk continued to remain a high-risk category, with its risk perception rising further in the latest survey. Operational risk and profitability risk also inched up marginally (Figure 2).

Figure 2: Systemic Risk Survey: Risks Identified

Chart 1: Confidence in the Stability of the Financial System

The latest survey shows that 66 per cent of respondents have expressed worsening confidence in the stability of the global financial system, much higher than the 28 per cent in the previous survey. The assessment of the Indian financial system was upbeat, as 92 per cent of them showed a higher or similar level of confidence in the Indian financial system (Chart 1 a and b).

  • Around 80 per cent of panellists expected better or similar prospects for Indian banking sector over the following year, marking an improvement from the previous survey round (Chart 2).

  • About 60 per cent of panellists expected the asset quality of banking sector to remain unchanged or improve marginally over the next six months, supported by an improved growth outlook, easy liquidity conditions, lower interest rates, and stable prospects for corporate lending. However, 40 per cent of respondents identified factors such as heightened global uncertainty, risks in the export sector, and stress in unsecured lending as potential downside risks to asset quality, thereby expecting a marginal deterioration (Chart 3 a).

Chart 2: Prospects of Indian Banking Sector in the Next Year

Chart 3: Indian Banking Sector – Outlook
  • Around 53 per cent of the respondents assessed the demand for credit to improve in the near-term owing to uptick in rural demand, better business sentiments and improved health of banks. Another quarter of the respondents reported credit demand to remain unchanged (Chart 3 b).

  • Regarding the impact of trade tensions and protectionist policies, three-fourths of the respondents assessed moderate impact of such disruptions on overall financial stability. However, around 88 per cent of participants expected trade slowdown to have a limited to moderate impact on banking sector asset quality (Chart 4 and 5).

  • Most of the respondents (around 80 per cent) perceived export-dependent manufacturing sectors (e.g. textiles, readymade garments, electronics) and MSMEs in export clusters to face the highest risk due to global trade disruptions. Nearly 40 per cent of respondents assessed the shipping and logistics industry to be the most vulnerable to trade slowdown (Chart 6).

Chart 4: Impact of Trade Tensions and Protectionist Policies on Overall Financial Stability

Chart 5: Effect of Trade Slowdown on Banking Sector Asset Quality

Chart 6: Sectors Vulnerable to Global Trade Disruptions

Risks to Financial Stability

Going forward, the respondents identified the following risks to financial stability:

  • Geopolitical conflicts

  • Capital outflows and impact on Indian rupee

  • Increase in trade tariffs and impact on global trade

  • Global growth concerns

  • Climate risk

  • Cybersecurity issues

  • Slowdown in domestic growth

  • Rise in US treasury bond yield


1 The risk perception, as it emanates from the systemic risk survey conducted at different time periods (on a half-yearly basis in May and November), may shift from one risk category to the other, reflected by the change in colour. However, within the same risk category (boxes with the same colour), the risk perception may also increase/decrease or remain the same, the shift being indicated accordingly through average numeric values.

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