reports_details - RBI - Reserve Bank of India
Motivation and Benefits
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.
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:
- investors benefit from access to markets where previously this was not possible
- liquidity lowering the required rate of return
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.
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 |
Bank # 2 |
||
|
Leverage |
30/1=30 |
Leverage |
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.
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).
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 |
100 |
Debt |
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 |
100 |
Debt |
200 |
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 |
100 |
Debt |
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 |
10 |
Debt |
10 |
Result:
- debt equity ratio improves dramatically
- B/S size is reduced
Other Benefits
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.
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.
(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.
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.
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.
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 persons 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.
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.
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:
- The use of capital can be optimised by reconfiguring portfolios to satisfy the risk-weighted capital adequacy norms better.
- 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.
- 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.
- 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.
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
Annex 3: Important Domestic Regulatory Measures
|
1. Reserve Bank of India (RBI)
2. Securities and Exchange Board of India (SEBI)
3. Insurance Regulatory and Development Authority of India (IRDAI)
4. Pension Fund Regulatory and Development Authority (PFRDA)
5. Insolvency and Bankruptcy Board of India (IBBI)
6. International Financial Services Centres Authority (IFSCA)
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. |
Annex 2: Methodologies
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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. (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, Ẑi,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. (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). 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
II. Credit concentration risk
III. Interest rate risk in trading book
IV. Interest rate risk in banking book
V. Liquidity risk
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. (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.
(b) Credit risk parameter For credit risk parameter, AMCs may subject the securities held by the scheme to the following:
(c) Liquidity risk parameter For liquidity risk parameter, the following analysis is being undertaken:
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:
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:  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,  1 The macro stress test is carried out on select 46 scheduled commercial banks (SCBs). |
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Annex 1: Systemic Risk Survey
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.
  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).
 
   Risks to Financial Stability Going forward, the respondents identified the following risks to financial stability:
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. |
Chapter III: Regulatory Initiatives in the Financial Sector
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The global financial system faces mounting challenges from trade tensions, cyber threats, and climate-related risks. In response, global regulators are working to build systemic resilience through strengthened Basel standards, improved liquidity management, enhanced cybersecurity, and comprehensive climate risk frameworks. Domestically, regulators are aligned with these efforts, focusing on digital fraud prevention, secure digital lending, and mutual fund reforms. The Financial Stability and Development Council (FSDC) and its Sub-Committee continues to play a vital role in building a resilient and secure financial system. Introduction 3.1 In response to growing economic uncertainty and structural shifts in the global financial landscape, regulators remain committed to enhance the resilience of the global financial system. Policymakers and global standard-setting bodies are advancing measures to strengthen the system’s resilience to complex securitisation structures, rapid technological changes, rising cyber threats and escalating climate-related risks. Since the December 2024 issue of Financial Stability Report, several regulatory initiatives have been undertaken in key areas including cyber security, cross-border payments, and climate-related risks. 3.2 Against this backdrop, this chapter reviews the recent major regulatory initiatives, both global and domestic, aimed at enhancing the stability and resilience of the financial system. III.1 Global Regulatory Developments III.1.1 Banking 3.3 The Basel Committee on Banking Supervision (BCBS) regularly reviews the impact of the Basel III standards on banks and publishes the results reflecting different degrees of implementation of these standards such as risk-based capital ratio, leverage ratio framework and disclosure requirements, liquidity metrics such as LCR and NSFR. The latest Basel III monitoring exercise covered both large international active banks (Group 1) and other smaller banks (Group 2). The results1 highlighted that for Group 1 banks, NSFR remained stable while the LCR decreased slightly. Group 2 banks2 showed an increase in both LCR and NSFR. 3.4 The BCBS also revised its principles for management of credit risk3 (‘Credit Risk Principles’) issued in 2000, to align them with the current Basel Framework and the latest guidelines issued by the Committee. The updated principles provide guidelines for banking supervisors to evaluate banks’ credit risk management processes in four key areas: (i) establishing a suitable credit risk environment; (ii) operating under a sound credit-granting process; (iii) maintaining an appropriate credit administration, measurement, and monitoring process; and (iv) ensuring adequate controls over credit risk. III.1.2 Financial Markets 3.5 The complex structuring and multi-layered distribution chains in certain securitisation structures create misaligned incentives between originator of securitised products and their investors while encouraging rapid and largely undetected build-up of leverage and maturity mismatches. To address such vulnerabilities, a recent evaluation report4 by the Financial Stability Board (FSB) assesses the extent to which G20 reforms on securitisation have achieved their financial stability objectives. The report reviews the implementation status of the International Organisation of Securities Commission (IOSCO) policy recommendations5 across FSB jurisdictions and revised prudential standards for bank exposures to securitisation in the residential mortgage-backed securities (RMBS) and collateralised loan obligation (CLO) markets. The report observes that though the reforms have improved the overall resilience of securitisation markets while increasing market transparency, it is difficult to definitively assess their resilience as these markets have not yet been tested through a full credit cycle. This is particularly relevant for CLOs, which have expanded rapidly but have not yet faced a prolonged downturn. The report has identified a few issues for consideration of national and international authorities, including: (a) monitoring risks in securitisation markets given the developments in synthetic risk transfers and private credit activity in securitisation structures; (b) risk retention requirements in CLO market, given that a large portion of global CLO issuance remains outside the scope of these requirements and often involves third-party risk financing; and (c) divergences in reform implementation across jurisdictions and the implications for regulatory consistency and effectiveness. 3.6 The IOSCO has assessed6 the implementation by market authorities7 of its earlier recommendations to develop regulatory tools for addressing challenges arising due to technological adoption, particularly with respect to improving surveillance capabilities on a cross-market and cross-asset basis. Key recommendations of the latest report include regular review and updation of surveillance capabilities by market authorities in the context of their own markets and trading environment and collective efforts by market authorities on strengthening their cross-border surveillance capabilities. 3.7 The IOSCO published its final report on IOSCO Standards Implementation Monitoring (ISIM) for Principles (6-7) relating to the regulator in April 20258. The IOSCO Assessment Committee, established in 2012, developed the ISIM review as a tool to monitor the implementation of the IOSCO Principles and Standards by member jurisdictions. The three IOSCO core objectives of securities regulation are protection of investors, ensuring that markets are fair, efficient, and transparent, and reduction of systemic risk. The ISIM review covered two IOSCO Principles (Principles 6 and 7) relating to the regulator: a. Principle 6: The regulator should have or contribute to a process to identify, monitor, mitigate and manage systemic risk, appropriate to its mandate. Principle 6 recognises that promoting financial stability is a shared responsibility amongst the financial sector regulatory community and the tools available to reduce systemic risk generally consist of strong investor protection standards and enforcement measures, disclosure and transparency requirements, business conduct regulation and resolution regimes, etc. The Principle explicitly recognises that securities regulators may not have the appropriate tools to address certain forms of systemic risk and, therefore, it is important that they cooperate with other regulators. Overall compliance with Principle 6 was generally high among the participating jurisdictions. In case of India, the report states, “India SEBI has a comprehensive process for identification, monitoring of various risk indicators, and contribution to financial stability encompassing multiple groups/ forums under the umbrella of the Financial Stability Development Council9 (FSDC) to analyse the various sources of risks, such as an Early Warning Group for detection of early warning signals, Forum for supervision of Financial Conglomerates, Technical Group for discussion of risks to systemic financial stability and inter-regulatory coordination, etc. India IFSCA is also a member of the FSDC and participates in the various groups such as FSDC Sub-Committee and Inter Regulatory Technical Group.” b. Principle 7: The regulator should have or contribute to a process to review the perimeter of regulation regularly. Principle 7 seeks to ascertain whether securities regulator performs a regular review of the perimeter of regulation, thereby promoting a regulatory framework that supports investor protection, fair, efficient and transparent markets, and the reduction of systemic risk. Overall, a high level of implementation by participating jurisdictions has been observed for Principle 7. India is among the participating jurisdictions that have affirmative answers to all the key questions relating to Principle 7, as summed up in the Report: “The regulatory review process in India is structured within the group of regulators around the working of its FSDC. Both India SEBI and India IFSCA are members of the FSDC. India SEBI, upon identification of any potential risks, also constitutes an expert committee/ working group. It also coordinates within formal frameworks of State Level Coordination Committees and Regional Economic Intelligence Committee with other financial/ non-financial authorities for information sharing.” 3.8 As part of the comprehensive efforts jointly taken by the BCBS, IOSCO and the FSB to improve transparency in derivatives, increasing the predictability of margin requirements and improving the liquidity preparedness of non-bank market participants for margin calls, policy prescriptions10 were issued on the initial margin in centrally cleared markets. The recommendations on initial margin, inter alia, include (a) availability of margin simulation tools to all clearing members; (b) disclosure of anti pro-cyclicality tools; and (c) identification of an internal analytical and governance framework appropriate to their business lines and risk profile, etc. 3.9 A joint report11 was also published on margins in non-centrally cleared markets. The report suggested industry practices to improve effectiveness of variation margin, especially during stress periods. These include resolving margin and collateral exchange issues, allowing flexibility in accepting non-cash collateral, adopting standardised and automated margin processes, and evaluating third-party services. To enhance initial margin responsiveness, the report suggests improvements in ISDA Standard Initial Margin Model (SIMM) including regular back testing, operational readiness for shortfalls and preparation for recalibrations. Besides, firms should also ensure sufficient liquidity to meet unexpected margin changes. III.1.3 Cyber Resilience 3.10 Cyberattacks and technology failures have become a significant threat to financial stability, especially in a world marked by rising digitalisation, evolving technologies and interconnectedness. Supervisory authorities need timely incident reporting to monitor such disruptions and coordinate effective responses and recovery efforts. Recognising the challenges posed by fragmented reporting frameworks across jurisdictions, the FSB has finalised a common framework12 to promote common information elements for incident reporting while allowing for flexible implementation practices. The Format for Incident Reporting Exchange (FIRE) encompasses a broad spectrum of operational incidents, including cyber incidents, and is designed to be applicable to third-party service providers and entities outside the financial sector. To support global implementation, the FSB has also issued a taxonomy package that uses the Data Point Model approach. Data Point Model is a data-centric method for organising objects hierarchically and can model various reporting scenarios derived from the underlying legal requirements in a business-friendly and non-technical manner. III.1.4 Climate Finance 3.11 Climate-related shocks have the potential to disrupt business operations through the materialisation of physical hazards, such as floods, droughts or windstorms (physical risks) and/ or due to changes in regulatory policies, technological innovation and/ or consumer preferences (transition risks). Climate shocks can interact with existing vulnerabilities in the financial system and threaten financial stability through various transmission channels and amplification mechanisms. In order to trace how physical and transition climate risks can be transmitted to the global financial system, the FSB has introduced an analytical framework13 for assessing climate-related vulnerabilities. The analytical toolkit sets out three high-level categories of metrics: a) proxies; b) exposure metrics; and c) risk metrics. Monitoring these metrics can provide early signals on potential drivers of transition and physical risks that can impact the financial system and quantify the scale of financial impacts. The report also compiles a set of forward-looking metrics currently used by the FSB jurisdictions to monitor climate-related vulnerabilities. Notable risk metrics for quantifying physical and transitions risks include carbon earnings at risk14 (used by the IMF and the Hong Kong Monetary Authority), climate beta15 and CRISK16 (used by the ECB). 3.12 The FSB also released a report17 on the transition plans, examining how firms’ climate transition strategies and their associated transition plans can support financial stability. Transition plans offer forward-looking insights into how financial and non-financial firms intend to align their operations with their climate goals. These plans can serve multiple functions: they inform firms’ strategic responses to climate risks, help investors make better-informed decisions by closing information gaps, and provide authorities with valuable data to monitor systemic risk and assess the alignment of financial flows with broader climate objectives. The FSB notes that the use of transition plans for financial stability assessment and macro-prudential analysis remains in its early stages and is currently limited to a small set of firms and shows wide variation in scope, methodology, and quality of key metrics. Enhanced comparability and consistency, supported by international standard-setting bodies, could significantly improve the usability of these plans for supervisory purposes, thereby reinforcing the financial system’s ability to manage climate-related risks over the long term. 3.13 The International Association of Insurance Supervisors (IAIS), an international standard-setting body, published an application paper18 highlighting the significance of climate risks for the insurance sector given their impact on the insurability of the assets under consideration as well as insurers’ own operations and investments. Also, on the other hand, opportunities exist for the insurance sector as it plays a critical role in the management of climate-related risks in its capacity as an assessor, manager and carrier of risk, and as an investor. The paper makes several recommendations in areas such as corporate governance, internal controls, scenario analysis, market conduct and public disclosures. 3.14 In January 2025, the International Auditing and Assurance Standards Board issued a new global sustainability assurance standard, the ‘International Standard on Sustainability Assurance (ISSA 5000)’, designed to strengthen the global sustainability disclosure ecosystem. The standard is designed to be used along with the International Ethics Standards for Sustainability Assurance (IESSA) issued by the International Ethics Standards Board for Accountants. ISSA 5000 contains principle-based requirements that support limited or reasonable assurance engagements of sustainability information reported by entities. The standards are profession agnostic and framework neutral, i.e., they can be applied in relation to sustainability information prepared under any suitable sustainability reporting framework. III.2 Initiatives from Domestic Regulators/ Authorities 3.15 During the period under review, financial regulators undertook several initiatives to improve the resilience of the Indian financial system (major measures are listed in Annex 3). III.2.1 Use of Indian Rupee for Cross Border Settlements 3.16 The Reserve Bank has progressively implemented a suite of measures to increase the use of Indian Rupee (INR) in cross-border settlements. In July 2022, in order to give a fillip to trade in INR, the Reserve Bank introduced the Special Rupee Vostro Account (SRVA) framework, an additional arrangement for effecting payment and settlement of exports/ imports in INR, by enabling foreign banks to open and maintain SRVAs with Indian banks, and with the additional provision of utilizing the INR balances therein for permissible capital and current account transactions. Use of INR for cross-border settlements was further bolstered by (i) notification of the Foreign Exchange Management (Manner of Receipt and Payment) Regulations in December 2023, which enables settlement of cross border transactions (other than those involving Nepal/ Bhutan and the ACU Mechanism) in any foreign currency (including local currencies of trading partner countries) alongside INR; and (ii) Memoranda of Understanding (MOU) with the central banks of the United Arab Emirates, Indonesia, Maldives and Mauritius to promote local currency settlement. 3.17 In continuation of the above initiatives, the Reserve Bank, in consultation with the Government of India, has further liberalised the FEMA framework as follows: (i) overseas branches of Authorised Dealer banks may open INR accounts for non-residents to conduct all permissible current and capital account transactions with Indian residents and for any transaction with a non-resident; and (ii) non-resident entities may utilise balances in their repatriable INR accounts (including SRVAs) to settle bona fide transactions with other non-residents and to invest in non-debt instruments, including foreign direct investment; and (iii) Indian exporters are now permitted to maintain foreign currency accounts abroad for receipt of export proceeds and use them for payment of imports. III.2.2 Prevention of Financial and Digital Payments Fraud 3.18 The rapid growth of digital transactions, though instrumental in enhancing convenience and efficiency, has been accompanied by a significant rise in financial frauds. The Reserve Bank, in conjunction with other regulatory agencies, has taken two major measures to combat financial and payments related frauds: (i) introduction of ‘.bank.in’ exclusive internet domain for Indian banks which helps customers identify legitimate bank websites and reduces the risk of phishing and other cyberattacks; (ii) steps to mitigate the misuse of mobile numbers of customers by fraudsters by directing the regulated entities to undertake transaction/ service calls and promotional voice calls only using ‘1600xx’ numbering series and ‘140xx’ numbering series, respectively. Additionally, SEBI has also advised its regulated/ registered entities to use only the ‘1600’ phone number series exclusively for service and transactional voice calls to their existing customers. 3.19 In further efforts to combat financial fraud using voice calls and SMS, RBI, as requested by Telecom Regulatory and Authority of India (TRAI), vide Circular ‘Prevention of financial frauds perpetrated through voice calls and SMS – Regulatory prescriptions and Institutional Safeguards’, advised the Regulated Entities to (a) make use of Mobile Number Revocation List19 (MNRL) published by Department of Telecommunication (DoT) to monitor and clean their customer databases and develop standard operating procedures for enhanced monitoring of accounts linked to revoked mobile numbers for preventing the linked accounts from being operated as Money Mules and/ or being involved in cyber frauds etc.; (b) provide their customer care number details to DoT for publishing in Digital Intelligence Platform (DIP) of DoT; (c) make marketing and transaction alert calls only from specific number series (as mentioned above) allotted to them by Telecom Service Providers (TSPs); and (d) undertake necessary awareness initiatives. III.2.3 Reserve Bank of India (Project Finance) Directions, 2025 3.20 To provide a harmonised framework for financing of projects in infrastructure and non-infrastructure (including commercial real estate & commercial real estate - residential housing) sectors by regulated entities (REs), the project finance directions were issued. The Directions lay down prudential framework for financing of projects, including treatment of RE exposures upon change in the date of commencement of commercial operations of such projects. III.2.4 Amendments to Liquidity Coverage Ratio (LCR) Framework 3.21 The banking turmoil20 in March 2023 highlighted, inter alia, the role of social media and digitalisation of financing in hastening the speed and impact of liquidity stress. Advances in digitalisation of finance have reduced friction, resulting in the actual scale and speed of the deposit outflows far exceeding the run-off rate assumptions under LCR framework. To address this concomitant increase in liquidity risk due to usage of technology, the Reserve Bank has undertaken calibrated amendments to the LCR framework by introducing additional run-off rate21 factors for internet and mobile banking enabled retail deposits (recognising their higher propensity for withdrawal). Haircuts on market value of Level 1 High-Quality Liquid Assets (HQLA) have also been calibrated to capture their liquidity generating capacity during periods of stress. These amendments are intended to improve the liquidity risk resilience of banks in India. III.2.5 Reserve Bank of India (Digital Lending Directions), 2025 3.22 As part of innovation in financial system, products, and credit-delivery methods, digital lending has emerged as a prominent way to design, deliver and service credit. However, unchecked third-party involvement, mis-selling, data-privacy breaches, unfair practices, exorbitant interest rates, and unethical recovery methods threaten public confidence in the digital-lending ecosystem. In this context, the Reserve Bank has issued Reserve Bank of India (Digital Lending) Directions, 2025 consolidating the previous instructions on Digital Lending and introduced two new measures for arrangements involving Lending Service Providers (LSPs) partnering with multiple regulated entities and for creation of a directory of digital lending apps (DLAs). The first measure aims to promote transparency and fairness in digital lending by enabling borrowers to compare loan offers objectively. It also aims to prevent biased or deceptive presentation of loan options by LSPs. The second measure aims to aid the borrowers in verifying the claim of a DLA’s association with a RE. III.2.6 Reserve Bank of India (Forward Contracts in Government Securities) Directions, 2025 3.23 Over the past few years, the Reserve Bank has been expanding the suite of interest rate derivative products available to market participants to manage their interest rate risks. In addition to Interest Rate Swaps, products such as Interest Rate Options, Interest Rate Futures, Interest Rate Swaptions, Forward Rate Agreements, etc. are available to market participants. To further develop the market for interest rate derivatives, forward contracts in government securities have now been permitted. Such forward contracts will enable long-term investors such as insurance funds to manage their interest rate risk across interest rate cycles. They will also enable efficient pricing of derivatives that use bonds as underlying instruments. III.2.7 Introduction of Mutual Funds Lite (MF Lite) Framework 3.24 A light-touch regulation regime for passively managed mutual fund schemes, ‘MF Lite Framework’ was introduced by SEBI with an intent to promote ease of entry, encourage new players, reduce compliance requirements, increase penetration, facilitate investment diversification, increase market liquidity and foster innovation. The framework is applicable to passive funds (with specific asset under management requirements) with underlying as domestic equity and debt indices and select commodity-based exchange traded funds (ETFs) such as gold and silver as well as fund of funds (FoFs) based on such ETFs. III.2.8 Introduction of Specialised Investment Funds 3.25 SEBI introduced a comprehensive regulatory framework for Specialised Investment Funds (SIF) aimed at bridging the gap between mutual funds and portfolio management services. SIFs are required to operate under a distinct brand name, logo and website, clearly differentiated from the mutual fund business. SIFs may offer investment strategies across equity, debt and hybrid categories. Comprehensive disclosure requirements include alternate month portfolio disclosures and scenario analysis for derivatives and risk depiction. This regulatory initiative is a significant step towards diversifying India’s pooled investment landscape. The introduction of SIFs is expected to encourage innovation in investment strategies while ensuring appropriate safeguards for investor protection and market integrity. III.2.9 Safer Participation of Retail Investors in Algorithmic Trading 3.26 SEBI issued a regulatory framework to facilitate safer participation of retail investors in algorithmic trading through brokers, which has outlined the rights and responsibilities of the main stakeholders of the trading ecosystem, viz., investors, stockbrokers, model providers/ vendors and market infrastructure institutions so as to enable use of algorithmic models by retail investors with appropriate safeguards. The said measure aims to enhance investor protection and promote market integrity. III.2.10 Identifying Unclaimed Assets 3.27 SEBI has put in place a framework in collaboration with National e-Governance Division (NeGD), Ministry of Electronics and Information Technology (MeitY) for ‘Harnessing DigiLocker as a Digital Public Infrastructure for reducing unclaimed assets in the Indian Securities Market’. Investors/ users can now download their mutual fund and demat holding statements as well as consolidated account statements through DigiLocker, the digital document wallet of the Government of India. By facilitating seamless access to financial records, this mechanism is expected to ensure the identification and reduction of unclaimed assets. By building on the centralised mechanism for reporting the demise of an investor through KYC Registration Agencies and the reforms to the nomination facilities in the Indian securities market, the current framework has been assisting the families and survivors of investors/ consumers after their demise. The SEBI has also developed a platform named ‘Mutual Fund Investment Tracing and Retrieval Assistant (MITRA)’ to provide investors with a searchable database of inactive and unclaimed mutual fund investor folios at an industry level, empowering the investors to identify the overlooked investments or any investments made by any other person for which he/ she may be the rightful legal claimant. The platform is aimed at reduction in the unclaimed mutual fund investor folios and incorporating mitigants against fraud risk. III.2.11 System Audit of Stock Brokers (SBs) through Technology-based Measures 3.28 The framework aims to strengthen and enhance the quality of system audit of stock brokers through online monitoring. Stock exchanges are required to develop a web portal to supervise system audit of stock brokers, wherein brokers and auditors will be mandated to provide details, such as date of appointment of auditor, audit official conducting the inspection, etc. during various stages of audit. The web portal shall capture geo-location of the auditor to ensure that the auditor visits the premises of the stock brokers physically for audit. Additionally, stock exchanges are also empowered to conduct surprise visits to verify the audit being actually carried out by the authorised auditor or authorised person of the audit firm. III.2.12 Access to Negotiated Dealing System – Order Matching (NDS-OM) 3.29 In order to further the objective of the Government of India to facilitate retail participation in purchase and trading of government securities, the SEBI has facilitated registered stock broker to access G-Secs market through NDS-OM under a Separate Business Unit (SBU). The securities market related activities of stock brokers would be segregated and ring-fenced from NDS-OM related activities of the SBU by way of maintenance of separate accounts and net worth. The framework ensures ease of doing investment for retail investors while ensuring ease of doing business for brokers. III.2.13 Intraday Monitoring of Position Limits for Index Derivatives 3.30 Position limits for various participants/ product types are specified by SEBI and these positions are monitored by the market infrastructure institutions (MIIs) at the end of day. In this situation, there is a possibility of undetected intraday positions (particularly on the day of expiry) beyond permissible limits, as end of day open positions will be negligible. Therefore, it was decided that in addition to the end of day monitoring mechanism, the position limits for equity index derivatives contracts will be monitored on an intraday basis from April 2025 onwards. The number of snapshots may be decided by the respective stock exchanges, subject to a minimum of four snapshots in a day. The snapshots will be randomly taken during pre-defined time windows. However, there shall be no penalty for breach of existing position limits intraday and such intraday breaches are not considered as violations. III.2.14 Operational Resilience of Financial Market Intermediaries 3.31 To streamline the reporting process of technical glitches across MIIs and facilitate the creation of a centralised repository of technical glitches, SEBI has developed a web-based portal, i.e., Integrated SEBI Portal for Technical Glitches (ISPOT), for submission of preliminary and final root cause analysis reports of technical glitches by the MIIs. This would help to improve the data quality, traceability of historical submissions related to technical glitches at the end of SEBI and MIIs, and preparation of system generated reports for monitoring of various compliance requirements in a more focused manner. SEBI has also stipulated a framework for business continuity for interoperable segments of stock exchanges. The said framework, inter alia, covers availability of identical or correlated trading products on another trading venue, creation of reserve contracts for scrips and single stock derivatives not traded on one stock exchange for invocation at the time of outage on the other stock exchange. III.2.15 Changes to Disclosure Requirements 3.32 SEBI introduced the ‘Additional Disclosures Framework’ for Offshore Derivative Instruments (ODIs) and FPIs with segregated portfolios, to address concerns of regulatory arbitrage. The concentration criteria and size criteria of the framework shall now be applicable directly to ODI subscribers. For FPIs with segregated portfolios, the concentration criteria shall be applied to each segregated portfolio independently. Further, issuance of ODIs (other than those with government securities as underlying) by FPIs shall be permitted only through a separate dedicated FPI registration, with no proprietary investments under such registration. ODI issuing FPIs shall neither issue ODIs with derivatives as reference/ underlying nor hedge their ODIs with derivative positions on stock exchanges. SEBI also enhanced the disclosure requirements for mutual fund schemes, mandating equity oriented mutual fund schemes to disclose Risk Adjusted Return (RAR) which shall be calculated as a ratio of portfolio rate of return less benchmark rate of return (i.e., excess return) to the standard deviation of this excess return. The move is aimed at making a holistic assessment of the portfolio manager’s level of skill and ability to generate excess returns. III.3 Other Developments III.3.1 Customer Protection 3.33 The number of complaints received by the Offices of the Reserve Bank of India Ombudsman (ORBIOs) for the previous two quarters indicates that majority of the complaints pertained to loans/ advances and credit cards constituting approximately 30 per cent and 18 per cent, respectively, of the complaints during Q3 and Q4 of 2024-25 (Table 3.1). 3.34 Complaints under the category ‘Loans and Advances’ and ‘Credit card’ emanated mainly due to, inter alia, revision in EMI without proper communication, excessive charges for delayed payments, inappropriate recovery practices, wrong/ delayed reporting of credit information and unsolicited credit cards. 3.35 With respect to the Indian securities market, the number of complaints received during Jan-Mar 2025 declined by 14.2 per cent over the previous quarter. Complaints related to stock brokers and listed companies accounted for 54.6 per cent of the total number of complaints received during the quarter (Table 3.2). 3.36 Under the SEBI Circular on Online Resolution of Disputes in the Indian securities market, MIIs are required to establish and operate a common Online Dispute Resolution Portal to harness online conciliation and online arbitration for resolution of disputes arising in the Indian securities market, the status of which is given in Table 3.3. III.3.2 Enforcement 3.37 During December 2024 – May 2025, the Reserve Bank undertook enforcement action against 177 REs (10 PSBs; 12 PVBs; three SFBs; one PB, three foreign banks, three RRBs; 118 co-operative banks; 22 NBFCs, one ARC, three HFCs and one CIC) and imposed an aggregate penalty of ₹29.15 crore for non-compliance with/ contravention of statutory provisions and/ or directions issued by the Reserve Bank. 3.38 During November 2024 - April 2025, prohibitive directions under Section 11 of the SEBI Act, 1992 were issued against 296 entities, while cancellation, suspension and warnings under SEBI (Intermediaries) Regulations, 2008 were taken against 23, six and one intermediaries, respectively. A total of 19 prosecution cases were filed during November 2024 - April 2025. Penalties under adjudication proceedings were imposed against 277 entities amounting to ₹38.5 crore during November 2024 to April 2025. III.3.3 Deposit Insurance 3.39 The Deposit Insurance and Credit Guarantee Corporation (DICGC) extends insurance cover to depositors of all the banks operating in India. As on March 31, 2025, the number of banks registered with the DICGC was 1,982, comprising 139 commercial banks (including 11 small finance banks, six payment banks, 43 regional rural banks, two local area banks) and 1,843 co-operative banks. 3.40 With the present deposit insurance limit of ₹5 lakh, 97.6 per cent of the total number of deposit accounts (293.7 crore) were fully insured and 41.5 per cent of the total value of all assessable deposits (₹241 lakh crore) were insured as on March 31, 2025 (Table 3.4). 3.41 The insured deposits ratio (i.e., the ratio of insured deposits to assessable deposits) was higher for co-operative banks (61.9 per cent) followed by commercial banks (40.4 per cent) (Table 3.5). Within commercial banks, PSBs had higher insured deposit ratio vis-à-vis PVBs. 3.42 Deposit insurance premium received by the DICGC grew by 12.1 per cent (y-o-y) to ₹26,764 crore during 2024-25 (Table 3.6), of which, commercial banks had a share of 94.7 per cent. 3.43 The Deposit Insurance Fund (DIF) with the DICGC is primarily built out of the premium paid by insured banks, investment income and recoveries from settled claims, net of income tax. DIF recorded a 15.2 per cent y-o-y increase to reach ₹2.29 lakh crore as on March 31, 2025. The reserve ratio (i.e., ratio of DIF to insured deposits) increased to 2.29 per cent from 2.11 per cent a year ago (Table 3.7). III.3.4 Corporate Insolvency Resolution Process (CIRP) 3.44 Since the provisions relating to the corporate insolvency resolution process (CIRP) came into force in December 2016, a total of 8,308 CIRPs have been initiated till March 31, 2025 (Table 3.8), out of which 6,382 (76.8 per cent of total) have been closed. Out of the closed CIRPs, around 20 per cent have been closed on appeal or review or settled, 18 per cent have been withdrawn, around 43.2 per cent have ended in orders for liquidation and 18.7 per cent have ended in approval of resolution plans (RPs). A total of 1,926 CIRPs (23.2 per cent of total) are ongoing. The sectoral distribution of corporate debtors under CIRP is presented in Table 3.9. 3.45 The outcome of CIRPs as on March 31, 2025 shows that out of the operational creditor initiated CIRPs that were closed, 63.6 per cent were closed on appeal, review or withdrawal (Table 3.10). 3.46 The primary objective of the Insolvency and Bankruptcy Code (hereinafter referred as “Code”) is rescuing corporate debtors in distress. The Code has rescued 3,624 corporate debtors (1,194 through resolution plans, 1,276 through appeal or review or settlement and 1,154 through withdrawal) till March 2025. It has referred 2,758 corporate debtors for liquidation. Several initiatives are being taken to improve the outcomes of the Code which have steadily increased the number of cases ending with resolution vis-à-vis cases in which liquidation is ordered (Chart 3.1). 3.47 Cumulatively till March 31, 2025, creditors have realised ₹3.89 lakh crore under the resolution plans, which is around 170.1 per cent of liquidation value and 93.41 per cent of fair value (based on 1082 cases, where fair value has been estimated). In terms of percentage of admitted claims, the creditors have realised 33 per cent. Furthermore, realisable value through RPs does not include (a) possible realisation through corporate and personal guarantors and recovery against avoidance transactions; (b) the CIRP cost; and (c) other probable future realisations, such as increase in value of diluted equity and funds infused into the corporate debtor, including capital expenditure by the resolution applicants. About 40 per cent of the CIRPs that yielded resolution plans were defunct companies. In these cases, the claimants have realised 152 per cent of the liquidation value and 19 per cent of their admitted claims.  3.48 Till March 2025, the total number of CIRPs ending in liquidation was 2,758, of which final reports have been submitted for 1,374 corporate debtors. These corporate debtors together had outstanding claims of ₹4.27 lakh crore, but the assets were valued at only ₹0.16 lakh crore. The liquidation of these companies resulted in realisation of 90 per cent of the liquidation value. The 1,194 CIRPs which have yielded resolution plans till March 2025 took an average of 597 days for conclusion of process, while incurring an average cost of 1.2 per cent of liquidation value and 0.8 per cent of resolution value. Similarly, the 2,758 CIRPs, which ended up in orders for liquidation, took an average 508 days for conclusion. III.3.5 Developments in International Financial Services Centre (IFSC) 3.49 To establish a world-class regulatory framework for firms operating in GIFT-IFSC, the International Financial Services Centres Authority (IFSCA) has issued 35 new regulations and 16 frameworks since 2021 which are aligned with international best practices. As of end-March 2025, the total number of registrations/ authorisations given by IFSCA has reached 865. 3.50 Nearly 161 Fund Management Entities (FMEs) registered in IFSC have launched 229 Funds (AIFs) with a total targeted corpus of US$ 50 billion. In terms of exchanges at IFSCA, the monthly turnover on GIFT IFSC Exchanges was US$ 95.30 billion in March 2025, whereas the average daily turnover of NIFTY derivative contracts on NSE International Exchange (NSE IX) was US$ 4.53 billion in the same period. A total of US$ 63.68 billion debt securities has been listed on the IFSC exchanges including US$ 15.43 billion of green bonds, social bonds, sustainable bonds and sustainability-linked bonds till March 2025. 3.51 The banking ecosystem at GIFT-IFSC comprises 29 banks (IFSC banking units), including 13 foreign banks, 16 domestic banks and one multilateral bank offering a wide spectrum of financial services. In addition to the banking units, two Global Administrative Offices (GAOs) are already operational in IFSC. The total banking asset size has grown from US$ 14 billion in September 2020 to US$ 88.7 billion in March 2025. The cumulative banking transactions have grown from US$ 53 billion in September 2020 to US$ 1.24 trillion till March 2025. 3.52 The India International Bullion Exchange (IIBX), a vibrant gold trading hub, has seen transactions and imports amounting to 101 Tonnes of Gold (equivalent to US$ 8.46 billion) and 1,147.98 Tonnes of Silver (equivalent to US$ 927 million). The registered aircraft leasing entities in GIFT-IFSC have grown to 33, while the total registered ship leasing/ ship financing entities have grown to 24 till March 2025. III.3.6 Pension Funds 3.53 The National Pension System (NPS) and Atal Pension Yojana (APY) have steadily grown, with increases in both subscriber count and assets under management. As of March 31, 2025, in terms of number of subscribers, NPS and Atal Pension Yojana (APY) have shown a growth of 14.2 per cent since March 2024, whereas the asset under management (AUM) has recorded a growth of 23.1 per cent in the same period. The combined subscriber base under NPS and APY has reached 8.4 crore in March 2025, with an AUM of ₹14.4 lakh crore (Chart 3.2), which is primarily invested in fixed income instruments (Chart 3.3). 3.54 The Unified Pension Scheme (UPS) as an option under NPS, was issued by the Department of Financial Services vide Notification dated January 24, 2025. In terms of Para 15 of the said notification, the PFRDA vide Gazette notification dated 19th March 2025 has issued PFRDA (Operationalisation of the Unified Pension Scheme under NPS) Regulations, 2025 and Central Recordkeeping Agencies has rolled out the processes for subscribers who are desirous of exercising choice of UPS.   III.3.7 Insurance 3.55 The life insurance sector has witnessed steady growth in premium income over the years, driven by factors such as increasing disposable incomes, regulatory reforms, improved ease of doing business and greater public awareness about the importance of insurance. The total insurance premium collected by life insurers increased to ₹8.7 lakh crore in 2024-25 from ₹8.3 lakh crore in 2023-24, registering a growth rate of 5.2 per cent. Similarly, new business premium of life insurance industry rose by 5 per cent, reaching ₹4.0 lakh crore in 2024-25 from ₹3.8 lakh crore in 2023-24. The total premium underwritten by general and health insurers reached ₹3.1 lakh crore in 2024-25 exhibiting a 6.2 per cent growth. Among various lines of business, the health insurance segment (which includes Overseas Medical Insurance) has experienced significant growth of 9 per cent. 3.56 The IRDAI (Maintenance of Information by the Regulated Entities and Sharing of Information by the Authority) Regulations, 2025 consolidate and replace the following three regulations: a) IRDA (Sharing of Confidential Information concerning Domestic or Foreign Entity) Regulations, 2012; (b) IRDAI (Maintenance of Insurance Records) Regulations, 2015; and (c) IRDAI (Minimum Information Required for Investigation and Inspection) Regulations, 2020. These consolidated regulations mandate electronic record-keeping with robust security and privacy measures, require regulated entities to adopt data governance framework and implement Board approved policies for record maintenance. 3.57 IRDAI has issued comprehensive guidelines allowing insurers to use equity derivatives to hedge their equity investment portfolios, thus safeguarding the market value of insurers’ equity holdings by mitigating the impact of market volatility. Further, IRDAI has introduced a new facility called “Bima Applications Supported by Blocked Amount” (Bima-ASBA). Under this mechanism, funds are blocked in the prospect’s bank account via a one-time UPI mandate and are transferred to the insurer only upon policy issuance. If the proposal is not accepted, the blocked amount is released, ensuring greater transparency and trust in the policy purchase process. 1 BCBS (2025), “Basel III monitoring report”, March. 2 Group 1 banks are those that have Tier 1 capital of more than €3 billion and are internationally active. All other banks are considered Group 2 banks. 3 BCBS (2025), “Principles for the management of credit risk”, April. 4 FSB (2025), “Evaluation of the Effects of the G20 Financial Regulatory Reforms on Securitisation”, January. 5 IOSCO’s policy recommendations in 2012 prescribed minimum risk retention requirements and standardised disclosure templates. Risk retention, or ‘skin in the game’, was identified as one way to address the misaligned incentives that was embedded in the ‘originate to distribute’ model of some securitisation products. 6 IOSCO (2025), “Thematic Review on Technological Challenges to Effective Market Surveillance Issues and Regulatory Tools”, February. 7 A statutory regulator, a self-regulatory organisation or the operator of a trading venue, responsible for conducting and/ or overseeing market surveillance efforts. 8 IOSCO (2025), “IOSCO Standards Implementation Monitoring (ISIM)”, April. 9 The Financial Stability and Development Council (FSDC) was set up by the Government as the apex level forum in December 2010 and is chaired by the Hon’ble Finance Minister. Members are Minister of State (Finance), Reserve Bank of India (RBI), Chief Economic Adviser to the Ministry of Finance, Securities and Exchange Board of India (SEBI), Insurance Regulatory and Development Authority of India (IRDAI), Pension Fund Regulatory and Development Authority (PFRDA), Insolvency and Bankruptcy Board of India (IBBI), International Financial Services Centre Authority (IFSCA), Secretaries of the Departments of (i) Economic Affairs, (ii) Financial Services, (iii) Revenue, (iv) Expenditure, (v) Ministry of Corporate Affairs and (v) Ministry of Electronics and Information Technology. 10 BCBS-CPMI-IOSCO (2025), “Transparency and responsiveness of initial margin in centrally cleared markets – review and policy proposals”, January. 11 BCBS-IOSCO (2025), “Streamlining VM processes and IM responsiveness of margin models in non-centrally cleared markets”, January. 12 FSB (2025), “Format for Incident Reporting Exchange (FIRE): Final report”, April. 13 FSB (2025), “Assessment of Climate-related Vulnerabilities: Analytical framework and toolkit”, January. 14 Shows the modelled increase in carbon costs relative to company earnings under different climate scenarios. 15 Reflects the sensitivity of financial or non-financial stock prices to climate transition or physical risks. 16 Expected capital shortfall of a financial institution in a climate stress generated via climate-related market and credit risk channels. 17 FSB (2025), “The Relevance of Transition Plans for Financial Stability”, January. 18 International Association of Insurance Supervisors (2025), “Application Paper on the supervision of climate-related risks in the insurance sector”, April. 19 MNRL comprises numbers that have been disconnected due to various reasons. 20 The March 2023 banking turmoil in the U.S. was characterised by the swift collapse of few U.S. banks, driven by rising interest rates and erosion of their bond portfolios, exacerbated by a heavy reliance on digital bank deposits which accelerated depositor withdrawals. 21 The runoff rate factor represents the estimated percentage of deposits a bank expects to be withdrawn or transferred during a period of stress. |
Chapter II: Financial Institutions: Soundness and Resilience
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The Indian banking sector remained robust with capital buffers at a record high, non-performing loans ratios at multi-decadal low, and improved operational performance. Macro stress tests reaffirm the resilience of banks to adverse scenarios. The resilience of the NBFC sector is bolstered by enhanced asset quality and healthy capital buffers. Interconnectedness among financial sector entities, as reflected in their bilateral exposures, continued to grow in double-digits. Introduction 2.1 The Indian financial sector remained strong and resilient amidst global headwinds. Banks and non-banking financial companies (NBFCs) reinforced their capital and liquidity buffers, while improving their asset quality. Bank credit growth decelerated and moved closer to deposit growth, narrowing the gap between both. The credit expansion by NBFCs was supported by improving credit quality and strong capital buffers. A favourable interest rate environment, conditioned by monetary policy easing, is expected to catalyse credit offtake, going forward. 2.2 This chapter presents stylised facts and analyses on latest developments in the domestic financial sector. Section II.1 outlines the performance of scheduled commercial banks (SCBs) in India through various parameters, viz., business mix; asset quality; concentration of large borrowers; capital adequacy; earnings; and profitability. Results of macro stress tests, sensitivity analyses and bottom-up stress tests performed to evaluate the resilience of SCBs under adverse scenarios are also presented. Sections II.2 and II.3 examine the financial parameters of urban cooperative banks (UCBs) and NBFCs, respectively, including their resilience under various stress scenarios. Sections II.4, II.5 and II.6 examine the soundness and resilience of the mutual funds, clearing corporations and insurance sector, respectively. Section II.7 concludes the chapter with a detailed analysis of the network structure and connectivity of the Indian financial system as well as contagion analysis under stress scenarios. II.1 Scheduled Commercial Banks (SCBs)1 2 3 4 2.3 SCBs’ aggregate deposits grew at 10.7 per cent (y-o-y) during 2024-25, notwithstanding a deceleration in respect of private sector banks (PVBs) and foreign banks (FBs) (Chart 2.1 a). Growth in term deposits continued to outpace that in current and savings account deposits (Chart 2.1 b). As on June 13, 2025, SCBs’ y-o-y deposits growth stood at 10.5 per cent. 2.4 SCBs’ credit growth decelerated in 2024-25 across bank groups (Chart 2.1 c). Credit growth of public sector banks (PSBs) outpaced that of PVBs during the year, after more than a decade. As on June 13, 2025, y-o-y credit growth of SCBs moderated to 9.6 per cent. The shares of agricultural and industrial loans in aggregate credit have contracted, while those of services and personal loans have expanded over the last fiscal year (Chart 2.1 d). Growth (y-o-y) in agriculture, services and personal loans has moderated over the last few quarters, while a marginal uptick is observed in the growth of industrial loans in March 2025 (Chart 2.1 e). Personal loans segment recorded broad-based deceleration in y-o-y growth, barring an uptick in the growth of other personal loans (Chart 2.1 f). Personal loans and services loans continued to remain the top two contributors to the overall credit growth of SCBs (Chart 2.1 g). Within personal loans, other personal loans have been the standout contributor, followed by housing loans (Chart 2.1 h).   II.1.1 Asset Quality 2.5 SCBs continued to record improvement in their asset quality, with the GNPA ratio and NNPA ratio5 declining to multi-decadal lows of 2.3 per cent and 0.5 per cent, respectively (Chart 2.2 a and b). The half-yearly slippage ratio, measuring new accretions to NPAs as a share of standard advances at the beginning of the half-year, remained stable at 0.7 per cent (Chart 2.2 c). The provisioning coverage ratio (PCR)6 of SCBs at 76.3 per cent in March 2025 (Chart 2.2 d) was marginally lower than that in September 2024. The write-offs to GNPA ratio7 for SCBs moved up marginally to 31.8 per cent in 2024-25 from 29.5 per cent in the previous year, led by PVBs and FBs, while write-offs by PSBs exhibited a marginal decline (Chart 2.2 e). Disaggregation of NPA movements revealed that write-offs8 were a major component of NPA reduction over the last 5 years (Chart 2.2 f).   II.1.2 Sectoral Asset Quality 2.6 SCBs’ asset quality exhibited broad-based improvement across bank groups in all major sectors, in terms of both GNPA ratio and stressed advances ratio9 (Chart 2.3 a). Agriculture sector continued to record the highest GNPA ratio and was the major contributor to the overall stock of GNPA. In the personal loans segment, asset quality remained broadly stable across major subsegments (Chart 2.3 b). Within the industrial sector, asset quality exhibited sustained improvement across all sub-sectors (Chart 2.3 c).   II.1.3 Credit Quality of Large Borrowers10 2.7 The credit quality of larger borrowers has improved steadily over the last few years and their share in total GNPAs of SCBs stood at 37.5 per cent in March 2025, while their share in overall credit of SCBs stood at 43.9 per cent (Chart 2.4 a). The large borrower cohort’s GNPA ratio declined from 3.8 per cent in September 2023 to 1.9 per cent in March 2025 (Chart 2.4 b). On a quarter-on-quarter (q-o-q) basis, while volume of SMA-1 loans increased, that of SMA-0 and SMA-211 loans and NPAs declined during March 2025 quarter (Chart 2.4 c). Correspondingly, SMA-2 ratio of large borrowers, that rose significantly in September 2024, led by PSBs, recorded a sharp decline in March 2025 (Chart 2.4 d). The proportion of standard assets to total funded amount outstanding has consistently improved over the past few years, reaffirming the positive shift in asset quality (Chart 2.4 e). The share of top 100 borrowers in total advances of SCBs remained stable at 15.2 per cent in March 2025 and none of them were classified as NPA.   II.1.4 Capital Adequacy 2.8 As of March 2025, the capital to risk weighted assets ratio (CRAR) of SCBs increased to a record high of 17.3 per cent (Chart 2.5 a). All bank groups reported higher CRAR in March 2025, compared to their September 2024 positions. The increase in CRAR during the quarter ending March 2025 can be attributed to higher growth in total capital relative to the growth in RWA during this period (Chart 2.5 b). CET1 capital ratio also increased across bank groups, indicating accretion of high-quality capital by banks (Chart 2.5 c). The overall tier 1 leverage ratio12 remained stable at 7.9 per cent (Chart 2.5 d). II.1.5 Earnings and Profitability 2.9 The profitability of SCBs remained strong in 2024-25, with profit after tax (PAT) increasing by 16.9 per cent (y-o-y). PAT of PSBs recorded a robust growth of 31.8 per cent, compared to much lower growth (9.2 per cent) for PVBs. PSBs’ higher profitability was primarily driven by a rise in their other operating income. On the other hand, higher growth in operating expenses was the key contributor to the relatively lower profitability of PVBs (Chart 2.6 a). 2.10 Net interest margin (NIM) declined driven by cost of funds even as yield on assets has remained stable (Chart 2.6 b, c and d). Both return on equity (RoE) and return on assets (RoA) ratios have declined in March 2025 (Chart 2.6 e and f).    II.1.6 Liquidity 2.11 SCBs have further improved their liquidity positions in March 2025, as evident from the strengthening of both liquidity coverage ratio (LCR)13 and net stable funding ratio (NSFR)14. Both LCR and NSFR have been comfortably above the regulatory minimum of 100 per cent across bank groups (Chart 2.7 a and b).  II.1.7 Resilience – Macro Stress Tests 2.12 Macro stress tests aim to assess the resilience of the banking system15 to macroeconomic shocks. The tests project capital ratios of banks under three scenarios - a baseline and two adverse macro scenarios over a two-year horizon, incorporating credit risk, market risk and interest rate risk in the banking book in the framework. The capital projections do not take into account any further planned recapitalisation by stake-holders or any future regulatory changes. While the baseline scenario is derived from the forecasted path of macroeconomic variables, the two adverse scenarios16 are hypothetical stringent stress scenarios derived by performing simulations using a VARX17 model (Chart 2.8).  (i) Adverse Scenario 1 (Geopolitical risk scenario): This scenario assumes a volatile global environment with heightened geopolitical risks and escalation of global financial market volatility. Supply chain disruptions adversely affect the commodity prices leading to rise in domestic inflation. The scenario further assumes that the domestic monetary policy tightens and the spread between lending rates and policy rate widens due to market instability. (ii) Adverse Scenario 2 (Global growth slowdown scenario): This scenario assumes a synchronised sharp growth slowdown in key global economies. Spillovers through trade and financial channels as well as market fragmentation dent domestic GDP growth. As a result, monetary policy eases to support growth. The scenario further assumes widening of lending spread due to higher uncertainty. 2.13 The macro stress tests results emphasise the resilience of SCBs to macroeconomic shocks. The results revealed that the aggregate CRAR of 46 major SCBs may marginally dip to 17.0 per cent by March 2027 from 17.2 per cent in March 2025, under the baseline scenario. It may decline to 14.2 per cent under adverse scenario 1, and to 14.6 per cent under adverse scenario 2. However, none of the banks would fall short of the regulatory minimum requirement of 9 per cent even under the adverse scenarios (Chart 2.9). 2.14 The CET1 capital ratio of the select 46 banks may rise from 14.6 per cent in March 2025 to 15.2 per cent by March 2027 under the baseline scenario. However, it may fall to 12.5 per cent under adverse scenario 1, and to 12.9 per cent under adverse scenario 2. None of the banks would breach the regulatory minimum requirement of 5.5 per cent under any of these scenarios (Chart 2.10).   2.15 The aggregate GNPA ratio of the 46 banks may marginally rise from 2.3 per cent in March 2025 to 2.5 per cent in March 2027 under the baseline scenario and to 5.6 per cent and 5.3 per cent, under adverse scenario 1 and adverse scenario 2, respectively (Chart 2.11).  II.1.8 Sensitivity Analysis18 2.16 Unlike macro stress tests, in which the shocks are applied in terms of adverse macroeconomic conditions, in sensitivity analyses, shocks are applied to single factors like GNPA, interest rate, equity prices and deposits, one shock at a time. This sub-section presents the results of top-down sensitivity analyses involving several single-factor shocks to assess the vulnerabilities of SCBs to simulated credit, interest rate, equity and liquidity risks under various stress scenarios19, based on their March 2025 position. a. Credit Risk 2.17 Credit risk sensitivity has been analysed under two scenarios wherein the system level GNPA ratio as of March 2025, is assumed to rise from its prevailing level by (i) one standard deviation (SD)20; and (ii) two SD in a quarter. Under a severe shock of two SD: (a) the aggregate GNPA ratio of 46 select SCBs moves up from 2.3 per cent to 7.9 per cent; (b) the system-level CRAR depletes by 370 bps from 17.2 per cent to 13.5 per cent; and (c) the CET1 capital ratio declines from 14.6 per cent to 11.0 per cent but remains well above the respective regulatory minimum levels. The system level capital impairment could be 22.6 per cent in this case (Chart 2.12 a). The reverse stress test showed that a shock of 4.6 SD would be required to bring down the system-level CRAR below the regulatory minimum of 9 per cent. A shock of 6.6 SD will be required to bring down the system-level CET1 capital ratio below the prescribed regulatory minimum of 5.5 per cent. Bank-level stress tests indicated that under the severe shock scenario (two SD), three banks with a share of 6.1 per cent in SCBs’ total assets may breach the regulatory minimum level of CRAR (Chart 2.12 b).  b. Credit Concentration Risk 2.18 Stress tests on banks’ credit concentration – considering top individual borrowers according to their standard exposures – show that in the extreme scenario of the top three individual borrowers of respective banks defaulting21, the system level CRAR would decline by 90 bps (Chart 2.13) and no bank would face a situation of a drop in CRAR below the regulatory minimum of 9 per cent. In this extreme scenario, four banks would experience a fall of more than two percentage points in their CRARs. 2.19 Under the extreme scenario of the top three group borrowers in the standard category failing to repay22, the system level CRAR would decline by 130 bps. No bank would witness a drop in CRAR below the regulatory minimum of 9 per cent (Chart 2.14).   2.20 In the extreme scenario of the top three individual stressed borrowers of respective banks failing to repay23, the system level CRAR would decline by 10 bps (Chart 2.15).  2.21 Credit concentration risk assessment, described above, evaluates banks’ resilience by considering defaults of top individual or group borrowers of respective banks and estimating impact on their CRARs. While this approach presents a conservative scenario by assuming that top borrowers of all banks default simultaneously, it does not explicitly capture the system-wide impact which a large borrower can cause as multiple banks can have exposure to a single entity. Box 2.1 provides a complimentary approach to address this scenario.
c. Sectoral Credit Risk 2.22 Shocks applied based on volatility of industry sub-sector-wise GNPA ratios indicate varying magnitudes of impact. By and large, sectoral credit risk remains muted — a two SD shock to basic metals and energy sub-sectors would reduce the system-level CRAR by 17 bps and 12 bps, respectively, whereas the impact of shocks on the rest of the sub-sectors is negligible (Table 2.1). 2.23 For the sample of 46 SCBs under assessment, the market value of investments subject to fair value has been on the rise and stood at ₹23.8 lakh crore in March 2025. Within the fair-valued investment portfolio, SCBs increased their allocation under the ‘fair value through profit and loss (FVTPL)’ category to 37.0 per cent in March 2025, and the remaining fair value portfolio (63.0 per cent in March 2025) was under the ‘available for sale (AFS)’ category. The rise in the share of the FVTPL portfolio under the revised framework29 is primarily attributable to a clearly identifiable held for trading (HFT) book which accounted for 90.9 per cent of the FVTPL portfolio. PSBs’ share in the fair-valued investment portfolio of SCBs continued its decreasing trend in the post-pandemic period with a sharp fall recorded immediately after framework revision, while the share of other bank groups witnessed an increasing trend (Chart 2.16).  2.24 Though the modified duration increased, the sensitivity (PV0130) of the AFS portfolio of SCBs diminished in March 2025, predominantly on account of decrease in AFS portfolio size as compared to September 2024. The PV01 of FVTPL (including HFT) portfolios of all banking groups increased because of a significant increase in market value of securities held in the portfolio (Table 2.2). The modified duration of the FVTPL portfolio decreased for all the banking cohorts. Variation in PV01 was higher for FBs. 2.25 An assessment of the impact of a parallel upward shift of 250 bps in the yield curve on the fair-valued portfolio (AFS and FVTPL (including HFT)) showed that the system level CRAR and CET1 capital ratio would reduce by 115 and 116 bps, respectively (Table 2.3). 2.26 All banking cohorts reported a sequential rise in trading profits in Q4:2024-25. The earnings from securities trading by PSBs and FBs was significant, as in the previous year, strengthening net operating income (Table 2.4). 2.27 Both the PSBs and PVBs have increased their holding of state development loans (SDLs)/ state government securities (SGSs) while paring their holdings in central government securities (G-Secs) and other HTM-eligible securities (Chart 2.17). 2.28 As at end-March 2025, the notional MTM gains in the HTM books of PSBs and PVBs together increased to ₹64,148 crore from ₹40,187 crore in September 2024. During the March 2025 quarter, unrealised gains rose across all categories of HTM book, benefiting from the falling yield curve. The unrealised gains of PSBs were predominantly in SDLs/ SGSs, as against those in G-Secs for PVBs (Chart 2.18).  2.29 If a shock of 250 bps parallel upward shift in the yield curve is applied, the MTM impact on the HTM portfolio of banks excluding unrealised gains/losses would reduce the system level CRAR and CET1 capital ratio by 313 bps each. However, no bank would witness a reduction in CRAR and CET1 capital ratio below the respective regulatory limits. 2.30 An assessment of the interest rate risk of banks31 using traditional gap analysis (TGA) on the rate sensitive global assets and liabilities and off-balance sheet items as of March 2025 showed that in a scenario of a 200 bps increase in interest rate, the earnings at risk (EAR) for PSBs and PVBs would be 13.3 per cent and 11.4 per cent of NII, respectively (Table 2.5). The impact would be minimal for FBs and SFBs. While the impact of an interest rate rise (fall) on earnings would be positive (negative) for PSBs, PVBs and FBs due to positive cumulative gap32 at bank group level, the impact for SFBs would be the opposite as the cumulative gap was negative.  2.31 As per the duration gap analysis33 (DGA) on the rate sensitive global assets, liabilities and off-balance sheet items, the market value of equity (MVE) for PVBs, FBs and SFBs would fall (rise) from an upward (downward) movement in the interest rate, while the effect on PSBs would be muted and the opposite. The MVE for SFBs would be particularly weighed down by an interest rate rise (Table 2.6). e. Equity Price Risk 2.32 As banks have limited direct capital market exposures owing to regulatory prescriptions, any impact of a possible significant fall in equity prices on banks’ CRAR would be low for the sample of 46 banks. Under scenarios of 25 per cent, 35 per cent and 55 per cent drop in equity prices, the system level CRAR would reduce by 25 bps, 35 bps and 55 bps, respectively (Chart 2.19 a). In the adverse scenario (shock 3), the lowest CRAR at bank level would be 13.6 per cent (Chart 2.19 b). Even if the entire capital market exposure is wiped out, the system level CRAR declines by 100 bps and CRARs of individual banks remain above the regulatory minimum level.  f. Liquidity Risk 2.33 Liquidity stress test attempts to assess the impact of a shock on liquidity positions of the select 46 SCBs, caused by plausible run on deposits, and increased demand for unutilised portions of committed credit and liquidity facilities. The baseline scenario for the stress test applies weights to each component as prescribed by the RBI guidelines on LCR computation34. Two stress scenarios are designed by applying higher weights (run-off rates) to certain cash outflow components35. The results showed that the aggregate LCR of the SCBs would fall from 132.1 per cent in the baseline scenario to 124.5 per cent in stress scenario 1 and further to 117.9 per cent in stress scenario 2 (Chart 2.20 a). Individually, all banks would be able to maintain LCR above the minimum requirement of 100 per cent in stress scenario 1, while one bank would marginally fall short to meet the same in stress scenario 2 (Chart 2.20 b).  II.1.9 Sensitivity Analysis of Small Finance Banks 2.34 Small Finance Banks (SFBs) consist of 11 entities whose collective share in total credit and total deposits36 are 1.5 per cent and 1.4 per cent, respectively, as of end-March 2025. Because of their small size, they are not represented in the list of 46 banks on which sensitivity analyses is typically performed. However, similar sensitivity analyses on credit risk and credit concentration risk performed separately for SFBs show that each SFB would remain resilient under stress scenarios. II.1.10 Bottom-up Stress Tests: Derivatives Portfolio 2.35 A series of bottom-up stress tests (sensitivity analyses) were conducted by select banks37, subjecting their derivatives portfolios as of March 2025 to four different shocks viz. two each based on interest rate and foreign exchange rate. In line with the trend observed in the recent past, the FBs maintained a significantly negative net MTM position as a proportion to CET1 capital at (-) 17 per cent in March 2025 compared with (-) 6 per cent in September 2024. For PSBs and PVBs, net MTM position was muted (Chart 2.21). For the overall system, the extent of negative MTM position increased in the half-year ending March 2025. 2.36 The impact of the interest rate shocks on the derivatives portfolios of the select banks, in terms of change in net MTM position, was found to increase in March 2025 over that in September 2024. The stress test results on the portfolios as of March 2025 showed that for the select banks, gain from an interest rate rise would be higher than loss from an interest rate fall of similar magnitude (Chart 2.22). As regards shocks of the rupee exchange rate on exposures to forex derivatives, the impact was noted to be reversed in March 2025 from that seen in September 2024.  2.37 The income from the derivatives portfolio includes changes in net MTM positions and the realised income. The contribution of the derivatives portfolio to the net operating income (NOI) of banks has increased significantly for all the bank groups in March 2025 as compared to September 2024. In particular, the realised income of FBs from derivatives portfolio formed a reasonable portion of their NOI despite their net negative MTM positions (Chart 2.23). Based on the notional principal amount, FBs had more diversified counterparties while most of the positions taken by PVBs and PSBs were with other banks.   II.1.11 Bottom-up Stress Tests: Credit, Market and Liquidity Risk 2.38 A suite of bottom-up stress tests (sensitivity analyses) was conducted by 37 select banks38 on their end-March 2025 position. The results affirmed the resilience of these banks to multiple types and magnitudes of shocks. All the sample banks would be able to meet the regulatory minimum requirement of CRAR under these scenarios (Chart 2.24).  2.39 The bottom-up stress test for liquidity risk revealed that liquid assets ratios39 of all the sample banks would remain positive under alternate shock scenarios, emphasising the adequacy of their HQLAs to withstand liquidity pressure from sudden and unexpected withdrawal of deposits. Under the scenarios of (i) a 10 per cent deposit run-off in 1-2 days and (ii) a 3 per cent deposit run-off for five consecutive days, the average liquid assets ratio of the select banks would drop from 23.0 per cent to 16.2 per cent and 12.5 per cent, respectively (Chart 2.25).  II.2 Primary (Urban) Cooperative Banks (UCBs)40 2.40 Credit extended by primary urban cooperative banks (UCBs)41 registered a higher y-o-y growth of 7.4 per cent in March 2025 than that in March 2024 (Chart 2.26 a). Both scheduled UCBs (SUCBs) and non-scheduled UCBs (NSUCBs) witnessed acceleration in credit growth. 2.41 The capital position of UCBs continued to strengthen in the post-pandemic period, with their CRAR rising to 18.0 per cent in March 2025. The strengthening of capital position has been broad based - across SUCBs and NSUCBs, as well as across all tiers42 - barring marginal dip for the Tier 1 UCBs (Chart 2.26 b and c). 2.42 The GNPA and NNPA ratios of UCBs, both SUCBs and NSUCBs, decreased significantly in March 2025 compared to September 2024 (Chart 2.26 d and e). A similar trend was observed in the GNPA ratio of large borrowers who account for 23.2 per cent of the UCBs’ loan book (Chart 2.26 f). The PCR also improved, rising from its levels in both March and September 2024, driven primarily by NSUCBs (Chart 2.26 g). Asset quality of UCBs improved across all tiers, alongside increase in PCR (Chart 2.26 h).    2.43 UCBs’ net interest margin remained the same in March 2025 as in September 2024, though it was slightly lower than the level recorded a year earlier (Charts 2.26 i). The RoA and RoE, however, declined compared to September 2024 as well as from their levels a year ago (Charts 2.26 j and k). In terms of tier-wise performance, RoA and RoE declined for Tier 1 and Tier 4 UCBs in March 2025, while both indicators saw an increase for Tier 2 UCBs (Chart 2.26 l). II.2.1 Stress Testing 2.44 Stress tests were conducted on a select set of UCBs43 to assess credit risk (default risk and concentration risk), market risk (interest rate risk in trading book and banking book) and liquidity risk, based on their reported financial positions as of March 2025. 2.45 Under the severe stress scenario of credit default risk, credit concentration risk and interest rate risk in the trading book, the system level CRAR would reduce from the pre-shock position of 17.4 per cent to 15.6 per cent, 14.1 per cent and 16.4 per cent, respectively. A severe interest rate shock in the banking book would reduce NII by 7.0 per cent at the system level. At the system level, the consolidated cumulative liquidity mismatch in 1-28 days’ time bucket would remain positive under severe stress. 2.46 One bank in the Tier 4 UCB sample - the largest category of UCBs with deposits above ₹10,000 crore - would not be able to meet the regulatory minimum requirement44 of 11 per cent CRAR under a severe stress scenario for credit default risk as well as for credit concentration risk. For Tier 2 and Tier 3 UCBs, the impact of credit risk and credit concentration risk under severe stress would be significant (Chart 2.27 a and b). None of the Tier 1 and Tier 4 UCBs would breach regulatory thresholds on CRAR under the interest rate shock scenarios applied to their trading book or face more than 20 per cent decline in NII from their banking books. Further, the impact on UCBs in other tiers would remain minimal (Chart 2.27 c and d). The smallest UCBs (Tier 1) exhibited resilience for all risk factors, except liquidity risk (Chart 2.27 e).   II.3 Non-Banking Financial Companies (NBFCs)45 2.47 The credit growth of NBFCs (Upper and Middle Layers) rose to 20.7 per cent (y-o-y) in March 2025 from 16.0 per cent in September 2024 but remained lower than the level observed in September 2023 (Chart 2.28 a). The acceleration in credit growth in March 2025 compared to the preceding half-year was driven by NBFC-UL. The surge in credit growth of NBFC-UL was partly on account of conversion of a housing finance company (HFC) to an upper layer NBFC, and merger of a middle layer NBFC with an upper layer NBFC. 2.48 Considering activity-based classification, credit growth for the second largest category of NBFCs (in terms of outstanding loans), viz., NBFC-IFCs has risen, vis-à-vis March 2024. NBFC-MFI’s portfolio contracted in H2:2024-25 as lenders exercised prudence in response to the stress in the portfolio (Chart 2.28 b).   2.49 Credit growth weakened across all major sectors excluding services and 'others', in H2:2024-25 (Chart 2.28 c). The credit in agriculture sector contracted. The rate of credit expansion by the NBFC-ML significantly declined across sectors in 2024-25, except 'others' category. 2.50 Credit growth in the unsecured personal loan segment has slowed down significantly since September 2023. Microfinance/SHG loans within the retail advances category has contracted in March 2025. Gold loans, on the other hand, have clocked rapid growth since September 2023 (Chart 2.29).  2.51 Delinquency level in both NBFC-UL and NBFC-ML improved (Chart 2.30 a). NBFC-ML continued to maintain higher PCR than NBFC-UL (Chart 2.30 b). GNPA ratio of Government-owned NBFCs (58.7 per cent share in advances by NBFC-ML) improved to 1.4 per cent while that of privately owned NBFCs of NBFC-ML remained at similar level (5.2 per cent) as in September 2024. At sectoral level, asset quality improved except in agriculture which contributed 3.4 per cent of the NBFCs’ GNPA (Chart 2.30 c). 2.52 The system level CRAR of NBFCs was healthy at 25.8 per cent in March 2025. NBFC-UL were consistently maintaining an elevated NIM at around 8 per cent, as against around 4 per cent by NBFC-ML. Consequently, profitability of NBFC-UL was much higher than that of NBFC-ML in terms of ROA and ROE. Profitability of NBFC-ML has declined significantly in H2:2024-25 as a few large MFIs in this layer recorded significant amount of loss in the second half of the year (Chart 2.31). 2.53 On the liquidity front, upper layer NBFCs were more vulnerable, given that they had a higher proportion of short-term liabilities to total assets in comparison with NBFC-ML. The share of long-term assets in total assets of NBFC-UL stood at 55.0 per cent as against nearly two-thirds for NBFC-ML. Higher share in case of NBFC-ML is due to the presence of NBFC-IFCs in this layer which predominantly lend for longer term projects and account for more than half of NBFC-ML’s loans (Chart 2.32).   2.54 The reliance of NBFCs on bank funding decreased over the last year as the impact of higher risk weight on bank lending to NBFCs played out. Dependence of NBFC-UL on bank borrowings and public deposits was higher than NBFC-ML (Table 2.7). II.3.1 Stress Test46 - Credit Risk 2.55 System level stress test under a baseline and two stress scenarios was conducted on a sample of 158 NBFCs47 over a one-year horizon for assessing the resilience of NBFC sector to credit risk shocks. While the baseline scenario was based on assumptions of business as usual, the medium and severe risk scenarios were derived by applying 1 SD and 2 SD shocks, respectively, to GNPA ratio.  2.56 Under the baseline scenario, the system-level GNPA ratio of the sample NBFCs may rise from 2.9 per cent in March 2025 to 3.3 percent in March 2026. Consequently, their aggregate CRAR may dip to 21.4 per cent in March 2026 from 23.4 per cent in March 2025 (Chart 2.33). Under the baseline scenario, 10 NBFCs (all in middle layer) having a share of 2.1 per cent of total advances of all NBFCs (UL + ML) may breach the regulatory minimum capital requirement of 15 per cent. Under the medium and severe risk scenarios, income loss and additional provision requirements may further reduce the CRAR compare to the baseline by additional 80 bps and 100 bps, respectively. Under the high-risk scenario, fifteen NBFCs (all in middle layer), having a share of 3.7 per cent of total advances of all NBFCs (UL + ML), may not be able to meet the regulatory minimum CRAR.  II.3.2 Stress Test48 - Liquidity Risk 2.57 The resilience of the NBFC sector to liquidity shocks was assessed by estimating the impact of assumed increase in cash outflows coupled with decline in cash inflows49. The results revealed that number of NBFCs which may experience negative cumulative liquidity mismatch of over 20 per cent in the next one year would be 1, 2 and 3 under the three scenarios, respectively (Table 2.8). II.4 Stress Testing of Mutual Funds 2.58 The Securities and Exchange Board of India (SEBI) has mandated that asset management companies (AMCs) should carry out stress testing of all open-ended debt schemes (except overnight schemes) every month to evaluate the impact of various risk parameters (viz., interest rate risk, credit risk, liquidity risk) related to such schemes on their net asset values (NAVs). The Association of Mutual Funds in India (AMFI) and each AMC specify the thresholds of impact for the risk parameters: breach of either the AMFI or the AMC threshold requires reporting and remedial action. 2.59 In April 2025, risk level of 43 open-ended debt schemes with total assets under management (AUM) of ₹2.25 lakh crore breached the AMFI or AMC prescribed threshold (Table 2.9). In this respect, all the mutual funds (MFs) have reported initiation of remedial action to be completed within the prescribed timeframe. 2.60 Furthermore, as part of liquidity risk management for open-ended debt schemes, two types of liquidity ratios, viz., (i) redemption at risk (LR-RaR), which represents likely outflows at a given confidence interval, and (ii) conditional redemption at risk (LR-CRaR), which represents the behaviour of the tail at the given confidence interval, have been used. All AMCs are mandated to maintain these liquidity ratios above the threshold limits which are derived from scheme type, scheme asset composition and potential outflows (modelled from investor concentration in the scheme). MFs are required to carry out backtesting of these liquidity ratios for all open-ended debt schemes (except overnight funds, gilt funds and gilt funds with 10-year constant duration) on a monthly basis. 2.61 The LR-RaR and LR-CRaR computed by top 10 AMCs (based on AUM) for 13 categories of open-ended debt schemes for March 2025 were well above the respective threshold limits for most of the MFs. A few instances of the ratios falling below the threshold limits were addressed by the respective AMCs in a timely manner (Chart 2.34). 2.62 Stress tests results and liquidity analysis of midcap and smallcap equity schemes of all MFs, published by AMFI, reveal that in April 2025, the number of days to liquidate 25 per cent of the portfolio for the top 5 schemes (in terms of AUM) ranged from 4 to 20 days for midcap schemes and 13 to 35 days for smallcap schemes (Table 2.10). II.5 Stress Testing Analysis at Clearing Corporations 2.63 Stress testing50 has been carried out at clearing corporations (CCs) to determine the segment-wise minimum required corpus (MRC), which needs to be contributed by clearing members (CMs) to the core settlement guarantee fund (Core SGF). MRC is determined for each segment (viz., equity cash, equity derivatives, currency derivatives, commodity derivatives, debt and tri-party repo segment) every month, based on stress testing.  2.64 The actual MRC for any given month is determined as the higher of the MRC of the month and the MRC arrived at any time in the past. Stress test analysis for the half-year during October 2024 to March 2025 indicated that the actual MRC requirement remained the same for most of the segments, except for the equity derivatives segment wherein the MRC requirement increased significantly due to the revised Circular by SEBI ensuring higher buffer to the probable losses in more adverse scenarios51 (Table 2.11). 2.65 The solvency ratio of an insurance company assesses the ability of the insurer to meet its obligations towards policyholders by reflecting the level of its assets over and above its liabilities. The minimum solvency ratio prescribed by the Insurance Regulatory and Development Authority of India (IRDAI) for insurance companies in India is 150 per cent. As insurance liabilities are contingent upon future events, a higher solvency ratio implies resilience of the insurer to withstand future uncertainties. 2.66 As of December 2024, and the previous three quarters, the aggregate solvency ratio for insurance companies remained above the prescribed threshold (Table 2.12). The solvency ratio of the life insurance companies remained at 204 per cent, while non-life insurance companies maintained a solvency ratio of 166 per cent as of December 2024. 2.67 Interconnections among financial institutions involve funding gaps arising due to liquidity mismatches and maturity transformation, payments processes, and risk transfer mechanisms. The financial system can be visualised as a network where financial institutions act as nodes and the bilateral exposures among them serve as links connecting these nodes. These links could be in the form of loans to, investments in, or deposits with each other, which act as a source of funding, liquidity, investment and risk diversification. While these links enable gains in efficiency and diversification of risks, they can become conduits of risk transmission and amplification in a crisis. Understanding the nuances in propagation of risk through networks is useful for devising appropriate policy responses for safeguarding financial and macroeconomic stability. II.7.1 Financial System Network52 53 2.68 The total outstanding bilateral exposures54 among the select 229 entities in the Indian financial system expanded at a growth rate of 19.6 per cent in March 2025 (Chart 2.35 a and b). 2.69 Long-term (LT) funding – primarily loans and advances, equity and LT debt instruments – was the key conduit for bilateral exposure in the system (Chart 2.36). A segment-wise analysis indicates that, in general, (a) LT loans continued to be mainly provided by SCBs to NBFCs; (b) AMC-MFs continued to be major investors in the equities issued by PVBs and NBFCs; (c) in the LT debt market, insurance companies held majority of instruments issued by PVBs, NBFCs and HFCs. In short-term (ST) funding, the inter-bank ST loans and deposits, CPs and CDs continued to be dominant. AMC-MFs continued to be the largest providers of funds in both the CP and CD markets. While AIFIs, NBFCs and HFCs were the largest receivers of fund in the CP market, PSBs, PVBs and AIFIs were the largest receivers in the CD market.   2.70 In terms of inter-sectoral net exposures55, AMC-MFs, insurance companies and PSBs remained the largest fund providers in the system and NBFCs, PVBs and HFCs were the largest receivers of funds. Among bank groups, PSBs and UCBs had net receivable positions whereas PVBs, FBs and SFBs had net payable positions vis-à-vis the entire financial sector (Chart 2.37).  2.71 The net receivable and net payable positions of leading fund providers and receivers recorded a gradual rise in March 2025 over a year ago (Chart 2.38).  a. Inter-Bank Market 2.72 Inter-bank exposures stood at 3.4 per cent of the total assets of the banking system in March 2025, at around the same level as in the past quarters. During H2:2024-25, fund-based exposure witnessed a significant increase, though its share in total bank assets remained stagnant, while non-fund-based exposures rose marginally (Chart 2.39).  2.73 PSBs continued to dominate the inter-bank market with more than 50 per cent share. The share of PSBs and FBs moderated with corresponding increase in the share of PVBs in H2:2024-25 (Chart 2.40).  2.74 Contrary to the dominance of the LT fund-based exposures in the overall financial network, the inter-bank market continued to rely heavily on ST funding – to the tune of 77 per cent of the fund-based inter-bank market as of March 2025. ST deposits and ST loans constituted more than 70 per cent of ST funds, while LT loans and LT Debt comprised a major share of LT funds (Chart 2.41 a and b).  b. Inter-Bank Market: Network Structure and Connectivity 2.75 The interconnection between entities in the inter-bank market network was highly skewed, with majority of banks having few links and few banks having many links, as reflected by the typical core-periphery network structure56 57. As of end-March 2025, one bank was in the inner-most core and nine banks in the mid-core circle consisting of PSBs and PVBs (Chart 2.42).  2.76 While the degree of interconnectedness among SCBs, measured by the connectivity ratio, remained unchanged in H2:2024-25, their local interconnectedness in terms of the cluster coefficient increased marginally (Chart 2.43).  c. Exposure of AMCs-MFs 2.77 Gross receivables of AMC-MFs, the largest fund providers, stood at ₹20.68 lakh crore (around 32 per cent of their average AUM) in March 2025 as against their gross payables of ₹1.26 lakh crore. SCBs (primarily PVBs) remained the major recipients of funds from AMC-MFs, followed by NBFCs, AIFIs and HFCs. More than half of the funding by the AMC-MFs continued to be in the form of equity holdings (Chart 2.44 a and b). d. Exposure of Insurance Companies 2.78 With gross receivables at ₹11.12 lakh crore against gross payables at ₹0.91 lakh crore, insurance companies were the second largest net providers of funds to the financial system as at end-March 2025. SCBs (primarily PVBs) were the largest recipients of their funds, followed by NBFCs and HFCs. Insurance companies provided funds mostly through LT debt and equity, accounting for 90 per cent of receivables, with limited exposure to ST instruments (Chart 2.45 a and b). e. Exposure to NBFCs (non-HFC) 2.79 NBFCs (non-HFCs) were the largest net borrowers of funds from the financial system, with gross payables at ₹21.15 lakh crore against gross receivables at ₹2.26 lakh crore as at end-March 2025. More than half of their funds continued to be sourced from SCBs, followed by insurance companies and AMC-MFs (Chart 2.46 a). 2.80 NBFCs (non-HFCs) garnered more than 70 per cent of funds through LT Loans and LT Debt, though the share of both continued to decline in H2:2024-25. The share of ST funding instruments (ST Loans and CPs) increased during the same period (Chart 2.46 b). f. Exposure to HFCs 2.81 HFCs, the third largest net borrowers, had gross payables at ₹6.00 lakh crore as against gross receivables of ₹0.14 lakh crore in March 2025. While SCBs continued to be the top fund providers, their share was seen to gradually decline with corresponding increase in funding from AMC-MFs. About 75 per cent of HFCs’ funds was sourced through LT loans and LT debt instruments (Chart 2.47 a and b).   g. Exposure of AIFIs 2.82 With gross payables and receivables at ₹9.06 lakh crore and ₹8.14 lakh crore, respectively, AIFIs were both active borrower and lender in the financial system and had net payable position of less than ₹1 lakh crore in March 2025. While the AIFIs raised funds mainly from SCBs, AMC-MFs and insurance companies, they were observed to lend to SCBs predominantly (83 per cent in March 2025) (Chart 2.48 a and b). II.7.2 Contagion Analysis 2.83 Contagion analysis uses network technology to estimate the systemic importance of different financial institutions. The failure of a bank due to solvency and / or liquidity losses could lead to contagion impact on the banking system along with the financial system depending upon the number, nature (whether it is a lender or a borrower) and magnitude of the interconnections that it has with the rest of the banking system.   a. Joint Solvency58- Liquidity59 Contagion Impact on SCBs due to Bank Failure 2.84 A contagion analysis of the banking network as at the end-March 2025 position indicated that the hypothetical failure of the bank with the maximum capacity to cause contagion losses would cause a solvency loss of 3.4 per cent of total Tier 1 capital of SCBs and liquidity loss of 0.3 per cent of total HQLA of the banking system (Table 2.13).  b. Solvency Contagion Impact on SCBs due to NBFC/ HFC Failure 2.85 As NBFCs (non-HFCs) and HFCs are among the largest borrowers of funds from the financial system, with a substantial part of funding from the banks, failure of any NBFC or HFC will act as a solvency shock to their lenders which can spread through contagion. 2.86 By end-March 2025, hypothetical failure of the NBFC with the maximum capacity to cause solvency losses to the banking system would have knocked off 2.9 per cent of the latter’s total Tier 1 capital but it would not lead to failure of any bank. Similarly, hypothetical failure of the HFC with the maximum capacity to cause solvency losses to the banking system would have knocked off 3.7 per cent of the latter’s total Tier 1 capital but without failure of any bank (Tables 2.14 and 2.15). 2.87 Further, in terms of the impact and vulnerability metrics developed for identification of impactful and vulnerable banks, two banks were common between the sets of top ten highly impactful banks and top ten highly vulnerable banks60 in March 2025. c. Solvency Contagion Impact after Macroeconomic Shocks to SCBs 2.88 Any contagion from failure of a bank is likely to get magnified if macroeconomic shocks result in distress to the banking system. In such a situation, similar shocks may cause some SCBs to fail the solvency criterion, which, then, acts as a trigger for further solvency losses. In the previous iteration, shock was applied to the entity that could cause the maximum solvency contagion losses. Here, we consider another iteration, where the initial impact on an individual bank’s capital is taken from the macro stress test61 results. The initial capital loss(+)/gain(-) stood at (-) 2.64 per cent, 13.83 per cent and 11.70 per cent of Tier I capital for baseline, adverse scenario 1 and adverse scenario 2, respectively. Further, all banks would be able to maintain Tier I capital ratio of 7 per cent under all three scenarios. It is observed that there would be no additional solvency losses to the banking system due to contagion (over and above the initial loss of capital due to the macro shocks). 1 Analyses are mainly based on data reported by banks through RBI’s supervisory returns covering only domestic operations of SCBs, except in the case of data on large borrowers, which are based on banks’ global operations. For this exercise, SCBs include public sector banks, private sector banks, foreign banks and small finance banks. 2 The analyses done in the chapter are based on the provisional data available as of June 10, 2025. 3 Private sector banks’ data for September 2023 quarter onwards are inclusive of merger of a large housing finance company with a private bank and, therefore, the data may not be comparable to past periods before the merger (applicable for all charts and tables). 4 Personal loans refer to loans given to individuals and consist of (a) consumer credit, (b) education loan, (c) loans given for creating/enhancement of immovable assets (e.g. housing, etc.) and (d) loans given for investment in financial assets (shares, debentures, etc.). 5 NNPA ratio is the proportion of net non-performing assets in net loans and advances. 6 PCR is the ratio of NPA provisions to GNPA. 7 Ratio of write-offs during the period to GNPA at the beginning of the period. 8 Write-offs include technical/prudential write-offs and compromise settlement, and may be subject to future recovery. 9 Stressed advances ratio is the ratio of total non-performing and standard restructured advances to total loans and advances. 10 A large borrower is defined as one who has aggregate fund-based and non-fund-based exposure of ₹5 crore and above to any single SCB. This analysis is based on SCBs’ global operations. 11 Special mention account (SMA) is defined as: 12 Tier I leverage ratio is the ratio of Tier I capital to total exposure. 13 Liquidity coverage ratio is defined as the ratio of stock of high-quality liquid assets (HQLA) to the total net cash outflow over the next 30 calendar days. 14 Net stable funding ratio is defined as the ratio of available net stable funding to required net stable funding. 15 Macro stress tests were conducted on a sample of 46 SCBs accounting for 98 per cent of the total assets of SCBs (excluding RRBs). 16 The shocks designed under adverse hypothetical scenarios are extreme but plausible. 17 VARX stands for Vector Autoregression with Exogenous Variables. See Annex-2 for detailed methodology. 18 Detailed methodology is provided in Annex 2. 19 Single factor sensitivity analyses are conducted for a sample of 46 SCBs accounting for 98 per cent of the total assets of SCBs (excluding RRBs). The shocks designed under various hypothetical scenarios are extreme but plausible. 20 The SD of the GNPA ratio is estimated by using quarterly data for the last 10 years. 21 In the case of default, the individual borrower in the standard category is considered to move to the sub-standard category. 22 In the case of default, the group borrower in the standard category is considered to move to the sub-standard category. 23 In case of failure, the borrower in sub-standard or restructured category is considered to move to the loss category. 24 The impact on the CRAR is estimated using additional provisioning needs and loss of interest income resulting from assets turning non-performing. 25 Chart 1 provides a visual explanation of CCRI calculation. 26 Chart 1 provides a visual explanation of reverse stress test using the curve. 27 Prior period consistency and comparability may be limited as historical data hasn’t been recast using the updated accounting standards. 28 The analysis in this portion is restricted to investments in India by the domestic operations of SCBs. Only interest rate related instruments for HTM, AFS and FVTPL (including HFT) portfolios and both interest and non-interest related investments for “Investment in Subsidiaries, Associates and Joint Ventures” are taken into account. 29 “Master Direction - Classification, Valuation and Operation of Investment Portfolio of Commercial Banks (Directions)” dated September 12, 2023. 30 PV01 is a measure of sensitivity of the absolute value of the portfolio to a one basis point change in the interest rate. 31 In terms of circular on “Guidelines on Banks’ Asset Liability Management Framework – Interest Rate Risk” dated November 04, 2010. 32 Gap refers to rate sensitive assets (RSA) minus rate sensitive liabilities (RSL). Advances, investments, swaps/ forex swaps and reverse repos are major contributors to RSA whereas deposits, swaps/ forex swaps and repos are observed to be the main elements under RSL. 33 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). 34 RBI circular no. RBI/2013-14/635 DBOD.BP.BC.No.120/21.04.098/2013-14 dated June 09, 2014, on “Basel III Framework on Liquidity Standards – Liquidity Coverage Ratio (LCR), Liquidity Risk Monitoring Tools and LCR Disclosure Standards”. 35 The stress scenarios are described in Annex 2. 36 Percentage of the total credit and deposit of SCBs (excluding RRBs) as of March 31, 2025. 37 Stress tests on derivatives portfolio are conducted by a sample of 36 banks (10 more banks have been included in the sample in this FSR to enhance the coverage considering that these banks had Rs 1,000 crore or more ‘Total Derivative Exposure’ on a continuous basis over the quarters), constituting active authorised dealers and interest rate swap counterparties. Details of test scenarios are given in Annex 2. 38 Stress tests are conducted by a sample of 37 banks (10 more banks have been included in the sample in this FSR to enhance the coverage). 40 Data are provisional and based on submission by UCBs through RBI supervisory returns. 41 Based on common sample of 1,294 UCBs covering over 90 per cent of gross loans extended by all UCBs. 42 Revised Regulatory Framework for Urban Co-operative Banks (UCBs) – Net Worth and Capital Adequacy (circular DOR.CAP.REC.No.86/09.18.201/2022- 23 dated December 01, 2022 and DOR.CAP.REC. No.109/09.18.201/2022-23 dated March 28, 2023). 43 The stress test is conducted with reference to the financial position of March 2025 for select 213 UCBs with asset size of more than ₹500 crore, excluding banks under the Reserve Bank’s All Inclusive Directions (AID). These 213 UCBs together cover around 72 per cent of the total assets of the UCB sector. The detailed methodology used for stress test is given in Annex 2. 44 The regulatory minimum CRAR for Tier 1 UCBs is 9 per cent and for the UCBs in Tier 2, Tier 3 and Tier 4 is 11 per cent. Further, UCBs in Tier 2, Tier 3 and Tier 4 shall achieve the CRAR of at least 12 per cent by March 31, 2026. 45 The analyses done in this section are based on the provisional data available for NBFCs in Upper Layer and Middle Layer excluding CICs, HFCs and SPDs, but includes companies presently under resolution as of June 10, 2025. Prior period consistency and comparability may be limited as NBFC data has been reclassified based on scale-based regulation. The effect of mergers and reclassifications, if any, has not been considered for recasting historical data. 46 The detailed methodology used for stress tests of NBFCs is provided in Annex 2. 47 The sample comprised of 158 NBFCs in the Upper Layer and Middle Layer with total advances of ₹26.94 lakh crore as of March 2025, which form around 95 per cent of total advances of non-Government NBFCs. The sample for stress test excluded Government NBFCs, companies presently under resolution, stand-alone primary dealers and investment focused companies. 48 The detailed methodology used for stress tests of NBFCs is provided in Annex 2. 49 Stress testing based on liquidity risk was performed on a sample of 244 NBFCs in the Upper Layer and the Middle Layer. The total asset size of the sample was ₹36.01 lakh crore, comprising around 99 per cent of total assets of non-government, non-CIC NBFCs in the sector. 50 The methodology used for stress testing at clearing corporations is given in Annex 2. 51 As per SEBI Circular on “Review of Stress Testing Framework for Equity Derivatives Segment for Determining the Corpus of Core Settlement Guarantee Fund (Core SGF)” dated October 01, 2024, SEBI introduced additional stress testing scenarios / methodologies for determining the Minimum Required Corpus (MRC) of Core SGF in the equity derivatives segment. The increase in values of MRC and Average Stress Test Losses observed from October 2024 in the Equity Derivatives Segments is due to such additional stress testing scenarios / methodologies. SEBI, vide letter dated May 03, 2024, had advised Clearing Corporation 1 to augment its Core-SGF in equity derivatives segment to at least ₹10,500 crore within six months. This was done after the study conducted by Clearing Corporation 1 which projected that its stress test losses could significantly rise over next three years. 52 The network model used in the analysis has been developed by Professor Sheri Markose (University of Essex) and Dr. Simone Giansante (Bath University) in collaboration with the Financial Stability Department, RBI. 53 The analyses are based on data of 229 entities from the following eight categories: SCBs, scheduled UCBs (SUCBs), AMC-MFs, NBFCs, HFCs, insurance companies, pension funds and AIFIs. Number of entities included are 88 SCBs, 20 SUCBs; 25 AMC-MFs (which cover more than 98 per cent of the AUMs of the mutual fund sector); 42 NBFCs (both deposit taking and non-deposit taking systemically important companies, which represent about 70 per cent of total NBFC assets); 22 insurance companies (which cover more than 95 per cent of assets of the sector); 17 HFCs (which cover more than 80 per cent of total HFC assets); 10 PFs and 5 AIFIs (NABARD, EXIM Bank, NHB, SIDBI and NaBFID). 54 Includes exposures between entities of the same group as well as different groups. Exposures are outstanding position as on March 31, 2025 and are broadly divided into fund-based and non-fund-based exposure. Fund-based exposure includes money market instruments, deposits, loans and advances, long-term debt instruments and equity investments. Non-fund-based exposure includes letter of credit, bank guarantee and derivative instruments (excluding settlement guaranteed by CCIL). 55 Inter-sectoral exposures do not include transactions among entities of the same sector in the financial system. 56 The diagrammatic representation of the network of the banking system is that of a tiered structure, in which different banks have different degrees or levels of connectivity with others in the network. The most connected banks are in the inner-most core (at the centre of the network diagram). Banks are then placed in the mid-core, outer core and the periphery (concentric circles around the centre in the diagram), based on their level of relative connectivity. The colour coding of the links in the tiered network diagram represents borrowings from different tiers in the network (for example, the green links represent borrowings from the banks in the inner core). Each ball represents a bank and they are weighted according to their net positions vis-à-vis all other banks in the system. The lines linking each bank are weighted on the basis of outstanding exposures. 57 88 SCBs and 20 SUCBs were considered for this analysis. 58 In solvency contagion analysis, gross loss to the banking system owing to a domino effect of hypothetical failure of one or more borrower banks is ascertained. Failure criterion for contagion analysis has been taken as Tier 1 capital falling below 7.5 per cent for SFBs, while 7 per cent for other banks. 59 In liquidity contagion analysis, a bank is considered to have failed when its liquid assets are not enough to tide over a liquidity stress caused by the hypothetical failure of a large net lender. Liquid assets are measured as: 18 per cent of NDTL + excess SLR + excess CRR. 60 The detailed methodology is given in Annex 2. 61 The contagion analysis used the results of the macro-stress tests and made the following assumptions: |
Chapter I: Macrofinancial Risks
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An uncertain and volatile global macroeconomic environment is testing the resilience of the global financial system. Global financial stability risks have increased as heightened policy and trade uncertainties have the potential to interact with existing vulnerabilities, especially elevated public debt, and amplify adverse shocks. The Indian economy and the financial system, however, continue to exhibit resilience, aided by strong macroeconomic fundamentals and a robust financial system. Risks emanating from global spillovers and escalation in geopolitical tensions and policy uncertainties remain a key concern. Introduction 1.1 Since the December 2024 Financial Stability Report (FSR), near-term global financial stability risks have risen significantly, driven by heightened geopolitical tensions and economic and trade policy uncertainties (Chart 1.1 a and b). Shifting US trade policies and lack of clarity surrounding its economic policies triggered a spike in volatility and sharp price declines across a range of markets. Consequently, financial conditions have tightened, and growth prospects have weakened. Though markets have recovered from the early-April lows due to sharp tariff hikes, considerable uncertainty persists about the evolution of trade patterns and economic outlook. Moreover, despite the recent market turmoil, asset valuations in several markets stay high relative to fundamentals and risks remain concentrated with exposures to a few large technology firms. Overall, global financial stability risks remain elevated, as unprecedented trade and policy uncertainties and unpredictability could potentially interact with the existing vulnerabilities - rising public debt, high leverage in the non-banking financial intermediaries (NBFIs) sector and stretched asset valuations - to amplify adverse shocks.  1.2 Amidst elevated global economic and trade policy uncertainties, the Indian economy continues to display resilience, underpinned by strong macroeconomic fundamentals and robust financial system. The economy is growing at a healthy pace, with the financial system meeting the financing needs of all sectors of the real economy. At the same time, domestic financial stability risks remain contained, as reflected in improving asset quality, strong capital and liquidity buffers and robust profitability of banks and non-bank lenders. The volatility in domestic financial markets also remained relatively low. 1.3 The domestic financial system, however, could be impacted by external spillovers. Growing trade disruptions and intensifying geopolitical hostilities could negatively impact domestic growth outlook and reduce the demand for bank credit, which has decelerated sharply. Moreover, it could also lead to increased risk aversion among investors and further corrections in domestic equity markets, which despite the recent correction, remain at the high end of their historical range. 1.4 Overall, while the broader financial system remains resilient, there is some build-up of stress primarily in financial markets on account of global spillovers. This is reflected in the marginal rise in the financial system stress indicator (FSSI), an indicator of the stress level in the Indian financial system, compared to its position in H1:2024-25 (Chart 1.2). 1.5 Against this backdrop, this chapter is structured into six sections. Section I.1 discusses evolving international and domestic macroeconomic developments and their implications for the near-term economic outlook. Section I.2 analyses the key trends and financial conditions across equity, bond and forex markets, while Section I.3 provides an assessment of corporate and household sector vulnerabilities. Sections I.4 and I.5 examine the stability of the banking and non-bank financial sectors, respectively. Section I.6 summarises the findings of the latest round of the systemic risk survey (SRS).  I.1.1 Global Outlook 1.6 The global macroeconomic outlook has deteriorated markedly amidst headwinds from persistent trade frictions, heightened policy uncertainty, and weak consumer sentiment. Despite some easing in tariff tensions on prospects of trade deals, the economic outlook remains fragile amidst elevated trade uncertainty. This could adversely impact consumer spending, business investment and financial conditions. The estimates of effective tariff rate on US merchandise imports have reached their highest level since 19381. The impact of such tariff measures, however, may vary across countries as tariffs constitute an adverse supply shock for the implementing countries and a negative demand shock for their trading partners2.  1.7 The global economy and the financial system have demonstrated exceptional resilience in the face of multiple shocks in recent years. However, the imposition of higher tariffs by the US has introduced a fresh shock to the global economy. The global output is, therefore, expected to remain below the historical average and inflation is projected to be above the long-term average in 2025 (Chart 1.3 a and b). Consequently, overall growth-inflation dynamics remain less than favourable relative to their long-run trends. 1.8 Citing escalation in trade tensions and elevated policy uncertainty, the International Monetary Fund (IMF) in its April 2025 World Economic Outlook has revised global growth projection downwards to 2.8 per cent in 2025 and 3.0 per cent in 20263 (Chart 1.4 a). Growth in both advanced economies (AEs) and emerging market and developing economies (EMDEs) is projected to decelerate. Consensus private sector forecasts, however, indicate a sharper deceleration in output growth (Chart 1.4 b). Furthermore, the IMF’s Growth-at-Risk (GaR) model, an important metric to assess risks to growth under extreme scenarios, indicates that there is a five per cent chance that global growth could fall below 0.4 per cent in the next one year4.  1.9 Other multilateral agencies have also lowered their global growth forecasts. The Organisation for Economic Co-operation and Development (OECD), in its Economic Outlook released in June 2025, has revised the global GDP growth forecast for 2025 by 20 basis points (bps) relative to its assessment in March 2025 report to 2.9 per cent. Similarly, the World Bank, in its June 2025 Global Economic Prospects (GEP), projected global GDP growth (using PPP weights) to decelerate from 3.3 per cent in 2024 to 2.9 per cent in 2025, lower by 30 bps relative to January 2025 projections. Moreover, the persistence of elevated trade frictions is expected to lower trade volumes going forward5, with the deceleration disproportionately concentrated in the US, China, and their closely linked regional trading partners. 1.10 Disinflation momentum has stalled, especially in AEs, where inflation generally remains above the central bank targets. Inflation in emerging market economies (EMEs), on the other hand, is mostly ruling below the targets (Chart 1.5 a). A slower retreat in services inflation, an uptick in core goods inflation and uncertainty around the impact of tariffs pose upside risks to global inflation. Nonetheless, the progress in disinflation so far has enabled central banks to pivot to monetary policy easing cycle in most jurisdictions (Chart 1.5 b). The US, however, remains an important exception, as it has held its policy rate constant in 2025 so far and markets expect fewer rate cuts this year. Overall, monetary authorities are charting out divergent policy trajectory, as they confront different trade-offs between growth and inflation.  1.11 Rising global public debt has been a recurring issue highlighted in recent FSRs and it remains a key concern, especially in the context of elevated uncertainty, slowing growth, rising debt servicing costs and growing spending pressures. According to the IMF, global public debt as a percentage of GDP is projected to reach above 95 per cent this year and 100 per cent by the end of the decade (Chart 1.6), while it may reach 117 per cent by 2027 in a severely adverse scenario6. In addition, the public debt in about one-third of the countries, which makes up 80 per cent of the global GDP, is currently larger than the pre-pandemic levels, driving the increase in global public debt7. Furthermore, countries with high levels of debt are also running large primary deficits (Chart 1.7). 1.12 Alongside the increase in debt levels, interest expenses as a share of government revenue remain elevated for most major AEs and EMEs (Chart 1.8 a and b). With debt levels projected to increase further as countries issue more debt to support economic activity, debt sustainability in those countries will be adversely impacted. The interest rate-growth rate differential is becoming increasingly adverse for debt sustainability in both the US and Europe (Chart 1.9). The rating agency Moody’s decision to downgrade the sovereign rating of the US citing sharp increase in debt, widening fiscal deficit and rising interest payments reflects this growing risk.    1.13 In this context, the smooth functioning of the sovereign bond markets, which must absorb larger bond issuances, is vital for financial stability. Sovereign bond markets are increasingly dominated by leveraged price-sensitive private investors even as constraints on banks to act as market makers and liquidity providers have tightened10. Thus, in times of stress, the resilience of market functioning will be tested (See paragraphs 1.23 to 1.25 for details). I.1.2 Domestic Outlook 1.14 The Indian economy, supported by strong macroeconomic fundamentals, remained the fastest growing major economy in the world during 2024-25. Moreover, as India’s growth is largely dependent on domestic demand, the impact of external shocks remained limited. In terms of growth outturns11 for 2024, India’s actual growth rate did not deviate significantly from projections even amidst deteriorating global outlook (Chart 1.10 a). The RBI has projected the real GDP to grow at 6.5 per cent in 2025-2612, same as in 2024-25, supported by buoyant rural demand, revival in urban demand, an uptick in investment activity on the back of above-average capacity utilisation, government’s continued thrust on capex and congenial financial conditions (Chart 1.10 b). The continued momentum in various high frequency indicators of services sector, robust agricultural production and above normal southwest monsoon forecasts, and strong goods and services tax (GST) collections underscore the sustained momentum and resilience of the economy.  1.15 The headwinds from protracted geopolitical tensions, elevated uncertainty and trade disruptions, and weather-related uncertainty pose downside risks to growth. Moreover, deceleration in global growth will act as a drag on domestic output. It is estimated that a 100 basis points (bps) slowdown in global growth can, ceteris paribus, pull down India’s growth by 30 bps13. 1.16 Domestic inflation has been steadily declining with the headline consumer price index (CPI) inflation recording a six-year low of 2.8 per cent in May 2025 (Chart 1.11). The outlook for food inflation remains favourable on account of softening prices and robust crop production. Moreover, the risk of imported inflation largely remains low with the anticipated slowdown in global growth likely to soften commodity and crude oil prices, although the recent escalation of geopolitical tensions in the Middle East has led to heightened uncertainty. The near-term and medium-term outlook gives greater confidence of a durable alignment of headline inflation with the target of 4 per cent, and it is likely to undershoot the target at the margin as per the projections of the RBI.   1.17 On the fiscal front, India’s public debt levels, primary deficit and share of interest payment in government revenue have remained relatively on the higher side compared to peer EMEs (Chart 1.12 a, b and c). However, India’s fiscal position and credibility has enhanced significantly in recent years on account of ongoing fiscal consolidation, improvement in the quality of expenditure and earmarking of debt-to-GDP as the nominal anchor for the central government’s fiscal policy. In addition, the government debt is predominantly rupee-denominated. The weighted average maturity of outstanding stock of central government market borrowings has risen from 10.4 years in 2018-19 to 13.2 years in 2024-2514 and around 97 per cent are issued at fixed rate15. Furthermore, unlike most other major economies, the flow data points to a lower debt trajectory supported by strong nominal GDP growth (Chart 1.13 a). Alongside, the favourable interest rate-growth rate differential of the central government augurs well for debt sustainability (Chart 1.13 b). 1.18 The resilience of the external sector has been a key contributing factor to India’s macroeconomic and financial stability. Current account deficit (CAD) at 0.6 per cent of GDP during 2024-25 remains eminently manageable, supported by sustained buoyancy in services exports and remittances. Moreover, current account balance turned into a surplus of 1.3 per cent of GDP in Q4:2024-25 (Chart 1.14).   1.19 In the capital account, high gross foreign direct investment (FDI) during 2024-25 indicates that India continues to remain an attractive investment destination. Net FDI flows, however, moderated due to higher repatriation and net outward FDI. Foreign portfolio investments (FPI) moderated during 2024-25. On the other hand, both external commercial borrowings (ECB) and non-resident deposits recorded higher inflows compared to the previous financial year (Table 1.1). Overall, net capital flows fell short of CAD during 2024-25, leading to a depletion in foreign exchange reserves. An update of the capital flows at risk framework16, which estimates the entire distribution of capital flows, shows that under extreme adverse shocks, with five per cent probability, the expected FPI outflows could reach 6 per cent of the GDP, while total capital outflows, that is, FPI and FDI, could be in the magnitude of about 7 per cent of GDP.  1.20 Notwithstanding the uncertainty surrounding the trade outlook, India’s external vulnerability indicators remain robust and continue to show improvement. Foreign exchange reserves at US$ 697.9 billion, as on June 20, 2025, are sufficient to cover more than 11 months of merchandise imports on BoP basis; external debt stood at a moderate 19.1 per cent of GDP at end-March 2025; the share of short-term debt on residual maturity basis stood at 45.4 per cent of foreign exchange reserves at end-March 2025; and net international investment position (IIP) improved (Chart 1.15 a and b). I.2.1 Global Financial Markets 1.21 The unsettling of the global trade outlook following the announcement of tariffs by the US in April 2025 created significant turbulence in global financial markets, as concerns about uncertain economic outlook and corporate profitability led to large sell off across multiple markets. Unlike previous risk-off episodes, traditional safe-haven assets such as the US treasuries fell, and the US dollar (USD) weakened. Equity markets, especially in the US, that have outperformed most global peers in the last five years, saw a sharp sell-off after the reciprocal tariff announcement in early April along with other AEs and EMEs (Chart 1.16 a). Global equity markets have since recovered on de-escalation in trade tensions. Long-term government bond yields rose after initially declining in a flight to safety, reflecting investors’ preference for cash and shorter-duration assets amid deteriorating fiscal outlook, especially in the US (Chart 1.16 b). Other segments of the financial markets were also affected by the turmoil as corporate bond spreads widened, prices of oil and copper fell, the market value of crypto assets declined, and open-ended investment funds and exchange-traded funds saw substantial outflows. This led to a tightening of financial conditions and significant bouts of volatility in financial markets, which has somewhat eased on the prospects of trade deals (Chart 1.16 c and d).  1.22 The April 2025 market turmoil brought into focus a few key market vulnerabilities. First, valuations of US stocks, which form nearly 55 per cent of global equity market17, remain stretched by historical standards. The forward price-to-earnings (P/E) ratio – the ratio of equity prices to expected 12-month earnings – is well above the historical median (Chart 1.17 a), and equity risk premium – a measure of additional return investors require to buy stocks relative to risk-free bonds – has declined to decadal low levels (Chart 1.17 b). Moreover, to justify current valuations, corporate earnings must grow at a robust pace, which may be difficult in an uncertain economic environment (Chart 1.17 c). Further price corrections and elevated volatility in US equities could spill over to other markets, especially EMEs like India.   1.23 Second, the core government bond markets, which are integral to the efficient functioning of global capital markets and the financial system, are exhibiting vulnerabilities driven by deterioration in market liquidity (Chart 1.18 a), increasing footprint of highly leveraged and price-sensitive NBFIs, and elevated volatility amid high levels of global public debt. In particular, the market liquidity in the US$ 29 trillion US treasury market, the largest and the most liquid bond market in the world, has been falling and dropped further in April 202518. Insufficient liquidity has the potential to amplify asset price volatility and cause significant price movements in reaction to shocks. This is also reflected in the widening bid-ask spreads as well as substantial daily change in bond yields (Chart 1.18 b and c). Alongside, the risk warehousing capacity of broker-dealers, firms that engage in the business of buying and selling securities either on their own behalf or on behalf of their clients, has decreased in recent times when compared with the size of trade flows, even as other non-bank liquidity providers appear to retract from filling up this gap in times of stress episodes19.  1.24 In recent years, hedge funds and other asset managers have taken on highly leveraged relative-value trades in US treasuries, such as basis trades and asset swap trades. These trades aim to take advantage of small differences in prices between the underlying cash market and derivatives market and involve in arbitraging the spread between treasury bonds and futures and treasury yields and interest rate swaps. The repo market is used for funding these trades and since price differences are small, they employ high leverage to improve returns. Due to their high leverage and exposure to spike in both futures margins and repo borrowing costs, these trades are a source of financial system vulnerability20. 1.25 Basis trades have almost doubled since March 2020 and swaps trades have incurred losses as spreads have not converged to zero (Chart 1.19 a and b). Moreover, these trades remain concentrated among a handful of hedge funds21. Concurrently, asset managers, such as mutual funds are also tapping treasury futures to enhance interest rate exposures, incentivised by the embedded leverage and high liquidity of futures contracts22. Increase in volatility in response to future shocks or shifts in risk sentiments can lead to disorderly unwinding of these trades, impacting smooth functioning of global bond markets. Moreover, risks can also spillover to the banking sector as hedge funds rely on banks, particularly globally systemically important banks (GSIBs), for more than 50 per cent of their total funding23 (Chart 1.19 c). 1.26 USD faced sharp depreciation pressure against most major currencies in the recent market turmoil (Chart 1.20 a and b). Typically, the USD tends to outperform other currencies in two entirely different scenarios; during periods of global stress as well as when the US economy exhibits exceptional growth, on the other hand it underperforms when global growth is strong relative to the US – the so-called ‘dollar smile’. This has been the defining framework for forex investors for a considerable period. However, in the current episode of exceptional economic uncertainty, the prices of US financial assets, including equities, have fallen forcing global investors to rebalance their portfolio. This has contributed to the depreciation of the USD, as growth slowdown fears and fiscal worries continue to weigh on the dollar.   Importantly, the correlation between the USD and the US treasury bond yields has diverged since the tariff announcements in April (Chart 1.20 c). In parallel, investors are increasingly hedging their holdings in dollar-denominated assets24, which could put further pressure on the USD. Moreover, there are structural changes happening in the global economy such as a major shift in the US trade policy and resetting of the global economic order. Thus, we could be entering uncharted territory in the global financial system as the USD’s primacy and safe-haven status are being challenged. I.2.2 Domestic Financial Markets 1.27 Domestic financial conditions tightened during January-March 2025, driven by widening of money and corporate bond market spreads (Chart 1.21 a). Since April 2025, financial conditions have eased supported by the Reserve Bank’s liquidity infusion measures and policy rate cuts. The Reserve Bank has injected durable liquidity amounting to about ₹9.5 lakh crore through suite of liquidity measures (open market operation purchases, buy-sell swaps and term variable rate repos) since January 2025, which led to system liquidity transitioning from deficit to surplus at end-March 2025. Additionally, the decision to cut cash reserve ratio (CRR) by 100 bps in a staggered phase will release ₹2.5 lakh crore of primary liquidity starting September till December 2025. Cumulatively, these measures have not only turned durable liquidity into surplus but will also contribute to faster transmission of monetary policy to the financial and credit markets (Chart 1.21 b and c).  1.28 Money market spreads have eased from the highs seen during January-March 2025, remaining near their long-term averages (Chart 1.22 a). Certificate of deposit (CD) spreads widened in the initial part of 2025 due to the tightness in system liquidity and large issuances of CDs by banks to bridge asset-liability mismatches (Chart 1.22 b). However, the easing of monetary policy and infusion of durable liquidity in recent months have narrowed the money market spreads. Notably, the spread between CDs and overnight indexed swaps (OIS) of similar maturity, a key metric of money market stress, has retreated from recent high. Similarly, the spread between commercial papers (CPs) issued by non-banking financial companies (NBFCs) and treasury bills of the same maturity also narrowed, reflecting surplus liquidity conditions.  1.29 The sovereign yield curve has bull steepened25, driven by faster disinflation and monetary policy easing (Chart 1.23 a). Consequently, term spreads rose (between 10-year and 2-year government bonds) to an average of about 24 bps during January – June 2025 (till June 10, 2025) from 9 bps during July-December 2024. The rise in US treasury yields along with the fall in India government bond yields has narrowed the spread between India and US 10-year treasury yields to a 20-year low (Chart 1.23 b). The bid-ask spreads on government bonds have softened, especially among semi-liquid and illiquid securities26, signaling improved trading conditions in the sovereign bond market (Chart 1.23 c).  1.30 The foreign exchange market witnessed bouts of volatility even as the USD/INR exchange rate recorded sharp two-way movements during January-May 2025. The pace of rupee depreciation accelerated in late 2024 and continued till February 2025. In March and April, however, it appreciated supported by the broad-based weakness of the USD and relatively better economic outlook for India vis-à-vis other economies (Chart 1.24). Different indicators, such as the real effective exchange rate (REER), the exchange market pressure (EMP) index27, implied volatility derived from option prices, and offshore-onshore spreads, continue to underscore the stability of the exchange rate (Chart 1.25 a, b, c and d). 1.31 Resource mobilisation through capital markets grew by 32.9 per cent to ₹15.7 lakh crore in 2024-25. Debt markets had the dominant share (63.5 per cent) in resource mobilisation, of which 99.2 per cent was raised through listed private placements (Table 1.2). Equity markets accounted for 27.4 per cent of total resource mobilisation. 1.32 The Indian equity market, which saw deep corrections between October 2024 and February 2025, owing to tepid earnings growth, FPI outflows and global sell-off, has largely recovered since March 2025. Nonetheless, as on June 10, 2025, most of the benchmark indices traded 3 to 8 per cent lower compared to their 52-week highs with the overall total market capitalisation down by 7 per cent from its peak in 2024 (Chart 1.26 a). Consequently, Indian equity market remained an underperformer compared to other major markets (Chart 1.26 b). Notably, despite the sharp decline in stocks, volatility remained relatively subdued until the recent spike triggered by geopolitical tensions and tariff-induced uncertainty (Chart 1.26 c). Furthermore, India’s weightage in the MSCI Emerging Markets (EM) Index has remained steady at 18.5 per cent as at end-March 2025 (Chart 1.26 d).   1.33 Amidst a global rebalancing of funds from EMEs’ equities28 to fixed income and developed markets29, Indian equity market, like other EMEs, saw consistent FPI outflows since October 2024 (Chart 1.27 a and b). Consequently, the foreign portfolio investors’ share in Indian equities has touched a decadal low, with domestic institutional investors’ (DIIs) share in overall ownership in all NSE-listed companies surpassing that of foreign portfolio investors (Chart 1.27 c and d).    1.34 During periods of heightened volatility, risk-off sentiments and sustained selling of Indian equities by the foreign portfolio investors, DIIs and individual investors (domestic households) have been providing strong support, thereby preserving market stability. 1.35 Equity valuations have moderated from their lofty levels, though they remain at the high end of historical range, especially for the midcap and smallcap stocks (Chart 1.28 a). Consequently, the gap between estimated earnings growth and required earnings growth for returning to historical valuation multiple has also reduced (Chart 1.28 b). Nonetheless, since earnings forecast updates more slowly than market prices and they are yet to reflect the prevailing geopolitical tensions and elevated uncertainty about the direction of tariffs, the current valuations may not be reflecting the extent of overvaluation (Chart 1.28 c). Moreover, the contribution of equity risk premium to returns remains high for midcap stocks (Chart 1.28 d). Thus, between earnings revisions and valuation compression, market impact could be significant in the event of adverse shocks. 1.36 Overall, as at end-March 2025, about two-thirds of stocks were trading with their P/E ratios higher than their respective benchmark P/E ratios (Chart 1.29). 1.37 The individual participation in Indian equities has increased in the last decade and the ownership pattern shows that their investments are diversified. However, their ownership share in microcap stocks far outweigh those in large, mid and smallcap stocks (Chart 1.30 a, b and c). Microcap stocks have a higher beta compared to other stocks and exhibit greater sensitivity to change in economic and financial conditions. Thus, market corrections could expose retail investors to greater volatility and amplify losses.   1.38 The growing participation of individual investors and associated risks in the equity derivatives segment were highlighted in June 2024 FSR. Since then, the SEBI has taken several important measures to strengthen this market segment, including but not limited to, rationalisation of weekly index derivatives products, increase in tail risk coverage on the day of options expiry, ensuring expiry of all index derivatives products on single day of the week, increase in contract sizes, upfront collection of option premium from buyers, removal of calendar spread treatment on the expiry day and intraday monitoring of position limits. Consequently, between December 2024 and March 2025, the average daily traded value by individuals and number of individuals trading per month declined by 14.4 per cent and 12.4 per cent, respectively, compared to an increase of 47.6 per cent and 101.8 per cent, respectively, between December 2023 and March 2024.  1.39 Geopolitical risk events often impact financial market variables. India’s equity market performance during global geopolitical episodes generally mirrors that of EMDEs compared to AEs. However, the interquartile range is relatively wider than EMDEs, indicating that stock returns exhibit more variability (Chart 1.31 a). Exchange rate movements, on the other hand, were smaller and more stable with a narrow interquartile range (Chart 1.31 b). The event study analysis of several past events corroborates the limited impact of such episodes on financial markets in India (Box 1.1).  1.40 In the debt market, corporate bond net outstanding rose to ₹53.6 lakh crore as at end-March 2025 with the highest ever fresh issuance of ₹9.9 lakh crore during 2024-25. Secondary market, however, remained lacklustre with average monthly turnover at 3.8 per cent of outstanding value (Chart 1.32 a). Listed private placements overwhelmingly remained the preferred route for resource mobilisation, while public issuances formed only a small fraction of total issuances (Chart 1.32 b). In 2024-25, AAA-rated firms dominated issuances with firms rated below AA constituting 16.0 per cent of the total issuances (Chart 1.32 c). Corporate bond spreads widened marginally due to tighter liquidity conditions, trade related uncertainty and softer growth prospects. Median spreads across rating categories were higher by 20-30 bps, even though yields softened (Chart 1.32 d). From a financial stability perspective, a deep and liquid corporate debt market is important as it provides an alternative to bank finance, widens investor base and improves overall resilience of the financial system.
 1.41 The development of a robust repo market enhances liquidity and efficiency in the corporate bond market. Accordingly, the AMC Repo Clearing Limited (ARCL) was operationalised in July 2023 as a Limited Purpose Clearing Corporation (LPCC) for providing clearing and settlement services as well as settlement guarantee for tri-party repo in corporate debt securities. The monthly trading volumes in this platform has seen robust growth (Chart 1.33). The ARCL platform also allows parties to offset their obligations through netting, and it provides a valuable tool for reducing risk, streamlining transactions and improving market efficiency. 1.42 Cyber security risk is a key vulnerability in securities markets. The expanding scale of digital financial services, cloud-based infrastructure and interconnected systems across sectors has exponentially increased the cyberattack surface. Given the systemic interconnectedness of financial entities and technology service providers, ensuring cyber resilience is critical to maintaining trust, stability and business continuity. As organisations increasingly depend on third party service providers for their business operations, vulnerabilities in the supply chain could pose systemic risk. Furthermore, the overreliance on a few major IT and cloud service providers has created dependency and vendor lock-in problems leading to concentration risks. Vulnerability in one system can quickly propagate across networks, affecting multiple entities. Phishing and social engineering attacks are evolving through Generative AI-powered methods, such as deepfakes and contextual frauds. Poorly secured Application Programming Interfaces (APIs), misconfigured databases, weak access controls and insider threats contribute to frequent data leaks and breaches, threatening both customer trust and regulatory compliance.  1.43 In this context, cybersecurity resilience will depend on the Security Operations Center (SOC) efficacy, risk-based supervision, zero-trust approaches and AI-aware defense strategies. Graded monitoring mechanisms, the use of behavioral analytics for threat detection, hands-on training, continuous learning and simulation-based exercises such as through Continuous Assessment-Based Red Teaming (CART), scenario-based resilience drills and uniform incident reporting frameworks are vital for enhancing the resilience of the digital ecosystem. Alongside, to ensure effective governance and preparedness, organisations must adopt measurable benchmarks like Cyber Capability Index and SOC Efficacy. I.3 Corporate and Household Sector I.3.1 Corporate Sector 1.44 Indian corporate sector remained resilient even as firms are navigating heightened trade policy uncertainty. Despite the moderation in sales growth of listed private non-financial corporates (NFCs), their operating profit margin remained solid (Chart 1.34 a and b). 1.45 Listed private NFCs’ debt serviceability improved as reflected in the healthy interest-coverage ratio32 (ICR) of firms across the manufacturing, services and information technology sectors (Chart 1.35). Furthermore, NFCs’ debt-service ratio33 remained one percentage point below historical average even as weighted average lending rate has risen by 162 bps since March 2022 to December 2024 (Chart 1.36 a). Moreover, their cash buffers34 remain sizeable (Chart 1.36 b).    1.46 At a broader level, vulnerabilities in the corporate sector remain contained with the debt-to-equity ratios of listed private NFCs consistently declining (Chart 1.37 a). When compared globally, India’s corporate debt-to-GDP ratio has been low compared to AE and EME peers (Chart 1.37 b). Moreover, the risk from unhedged ECBs has reduced with their share falling to 26.1 per cent in March 2025 compared to 32.9 per cent in September 202435. The trade policy uncertainty, however, is likely to impact earnings estimates, which have already been moderating in the recent past. The higher effective tariff rates are likely to put pressure on corporate margins going forward (Chart 1.37 c).  I.3.2 Household Sector 1.47 India’s household debt has been increasing in recent years, driven by rising borrowing from the financial sector. However, as on end-December 2024, India’s household debt at 41.9 per cent of GDP (at current market prices) was relatively low compared to other EMEs (Chart 1.38). 1.48 Among broad categories of household debt, non-housing retail loans, which are mostly used for consumption purposes36, formed 54.9 per cent of total household debt37 as of March 2025 and 25.7 per cent of disposable income as of March 2024 (Chart 1.39 a and b). Moreover, the share of these loans has been growing consistently over the years, and their growth has outpaced that of both housing loans and agriculture and business loans (Chart 1.39 c).  1.49 Housing loans, on the other hand, formed 29.0 per cent of household debt and their growth has been steady. However, disaggregated data shows that incremental growth has been mainly driven by the existing borrowers who are availing additional loans, and their share has increased to more than a third of the housing loans sanctioned in March 2025 (Chart 1.40 a). Moreover, share of borrower accounts with loan-to-value (LTV) ratios greater than 70 per cent is also rising (Chart 1.40 b), and delinquency levels are higher for lower-rated and more leveraged borrowers. However, these have declined considerably from their levels during COVID-19 (Chart 1.40 c).     1.50 At an aggregate level, the per capita debt of individual borrowers38 has grown from ₹3.9 lakh in March 2023 to ₹4.8 lakh in March 2025 (Chart 1.41 a). The rise in per capita debt has been mainly led by the higher-rated borrowers (Chart 1.41 b). 1.51 The share of better-rated customers (prime and above) among total borrowers is growing, both in terms of the outstanding amount and number of borrowers (Chart 1.42 a and b). This is important from a debt serviceability and financial stability perspective, as it indicates that household balance sheets at an aggregate level are resilient. 1.52 An update of the analysis of financial wealth of Indian households40 shows that the financial wealth of households grew sharply in 2023-24 (Chart 1.43 a). Since Q3:2019-20, asset price gains contributed to around one-third of the increase in the financial assets, while the remaining was on account of an increase in financial savings (Chart 1.43 b). Deposits and insurance and pension funds formed nearly 70 per cent of household financial wealth as at end-March 2024 even as the share of equities and investment funds has increased (Chart 1.43 c).  1.53 Overall, the risks to the Indian financial system from lending to households remain contained with easing monetary policy cycle likely to reduce debt service pressures on borrowers going forward. However, the trend in household debt accumulation, especially among lower-rated borrowers, requires close monitoring.  I.4 Banking System41 1.54 The resilience of the banking system has been pivotal to the strength of the Indian financial system. This is evident in scheduled commercial banks’ (SCBs) strong capital and liquidity buffers, improved asset quality and robust earnings (Chart 1.44). Adequate high quality common equity tier 1 (CET1) capital, declining loan losses and credit costs, and solid profitability lend credibility to their soundness and ability to lend to households and businesses as well as absorb losses in the event of downside risks (Chart 1.45 a, b, c and d). 1.55 Notwithstanding the solid performance of banks during the last three years, they could face some pressure in the near-term: (1) easing monetary policy cycle could impact the net interest margin (NIM) as growing share of loan book is linked to the external benchmark-based lending rate (EBLR), which is reset more frequently with change in repo rate. On the other hand, term deposits, which are also growing, have fixed contractual rates that change less frequently (Chart 1.46 a). The recent 100 bps cut in CRR, however, will cushion this impact by releasing funds for banks and reducing their costs; (2) credit growth has slowed, and credit impulse43 has turned negative (Chart 1.46 b). Economic slowdown, if any, amidst heightened uncertainty could drag credit demand lower, which may impact asset quality and profitability; and (3) banks’ liability profile is changing with the share of higher-cost term deposits and CDs growing compared to low-cost current account and savings account (CASA) deposits (Chart 1.46 c).   1.56 Post-pandemic, bank loan growth was largely driven by lending to the retail and services sector, particularly through unsecured retail loans and lending to the NBFCs. Pursuant to the RBI’s decision to increase risk weights on certain segments of consumer credit and bank lending to the NBFCs, loan growth in these two sectors has fallen sharply, contributing to a slowdown in total loan growth (Chart 1.47 a and b). Overall, a more cautious approach by lenders, improvement in lending standards, and the restoration of risk weights on bank lending to NBFCs44 are stability-enhancing and credit positive.  1.57 Even as unsecured retail lending has moderated – it forms 25.0 per cent of retail loans and 8.3 per cent of gross advances – its asset quality has relatively weakened compared to the overall retail portfolio - gross non-performing asset (GNPA) ratio at 1.8 per cent vis-à-vis 1.2 per cent in March 2025 - especially in respect of private sector banks (PVBs) (Chart 1.48 a and b). On the other hand, the SMA ratio, an indicator of possible stress build-up in loan book, has risen, led by public sector banks (PSBs) (Chart 1.48 c).   1.58 Slippages in unsecured retail loans remain elevated for PVBs. Fresh slippage in unsecured retail loans continues to dominate the overall slippage in retail loan segment with PVBs’ contribution significantly higher among bank groups (Chart 1.49 a). Alongside, write-offs continue to remain a key contributing factor to NPA reduction in the unsecured retail portfolio, especially among PVBs (Chart 1.49 b, c and d).   1.59 The share of floating rate loans in total gross advances of fourteen select banks, accounting for around 79 per cent of the assets of SCBs (excluding SFBs and regional rural banks), has increased from 72.0 per cent in March 2023 to 75.7 per cent in March 2025. The share of floating rate loans in the retail loan category rose from 60.2 per cent to 65.1 per cent during the same period - out of this, around 90 per cent are EBLR loans (Table 1.3 and 1.4). Thus, with faster transmission of monetary policy, the debt service burden of retail borrowers is expected to ease. 1.60 Despite a broad deceleration in bank credit growth, the share of credit to the micro, small and medium enterprises (MSME) sector in total non-food bank credit has been growing steadily and its growth has outpaced that in other sectors during 2024-25 (Chart 1.50 a and b). Within the MSME sector, however, credit to the micro enterprises, which formed 49.0 per cent of total credit to the MSME sector, witnessed slower incremental growth in 2024-25 compared to small and medium enterprises (Chart 1.50 c and d). 1.61 Asset quality has shown improvement with gross NPA ratio of MSME portfolio of SCBs falling from 4.5 per cent in March 2024 to 3.6 per cent as at end-March 2025 (Chart 1.51 a). This is also reflected in the significant moderation in SMA-2 ratio, an indicator of incipient stress (Chart 1.51 b). 1.62 In terms of amount outstanding, the share of sub-prime borrowers in the MSME portfolio of the SCBs has decreased from 33.5 per cent in June 2022 to 23.3 per cent in March 2025. PSBs, however, had a higher share of sub-prime borrowers in their MSME portfolio compared to PVBs and NBFCs (Chart 1.52 a and b).   1.63 The government’s credit guarantee schemes improved flow of credit to the MSME sector, especially vulnerable enterprises, with approximately ₹6.28 lakh crore guaranteed under two flagship schemes, viz., the Credit Guarantee Fund for Micro Units (CGFMU) and the Emergency Credit Line Guarantee Scheme (ECLGS). The NPA ratio in both schemes remains contained despite the riskiness of borrowers (Chart 1.53). 1.64 Consumer segment loans grew at a CAGR of 20.4 per cent between March 2021 and March 2025 compared to 14.6 per cent growth in the overall loans. During this period, loans extended by banking sector to this segment grew at a CAGR of 18.8 per cent (Chart 1.54 a). Consumer segment loan growth, however, has slowed following the implementation of regulatory measures by the RBI in Q3:2023-24, across lender types, product types and credit active consumers (Chart 1.54 b, c and d).   1.65 Even as loan growth to consumer segment slowed down, the quality of the portfolio has improved. Delinquency levels, except credit cards, have decreased, upgradations from SMA-2 accounts have risen, and slippages from SMA-2 accounts have fallen (Chart 1.55 a, b and c). The GNPA ratio of the SCBs’ consumer segment loans stood at 1.4 per cent in March 2025. Moreover, in a sign of improving underwriting standards, the share of borrowers rated prime and above increased for both PSBs and PVBs (Chart 1.56).   1.66 With the microfinance sector under stress, credit to the sector decreased by 13.9 per cent in 2024-25 (Chart 1.57). Adoption of tighter underwriting standards by the lenders was the primary driver behind deceleration in credit growth, which also resulted in a decrease in total active borrowers by 40 lakhs. Bank credit45 to the sector, which forms 48.3 per cent of total credit outstanding to the sector, contracted by 13.8 per cent in 2024-25.    1.67 The ratio of stressed assets in the microfinance sector increased in H2:2024-25, with 31-180 days past due (dpd) rising from 4.3 per cent in September 2024 to 6.2 per cent in March 2025 (Chart 1.58 a). The banking sector also saw an increase in stress in their microfinance loan book with 31-180 dpd rising from 4.7 per cent in September 2024 to 6.5 per cent in March 2025. However, borrower indebtedness, measured by the share of borrowers availing loans from three or more lenders, is showing a declining trend (Chart 1.58 b). 1.68 Overall, the resilience of the banking system has improved, as indicated by the banking stability indicator (BSI), which strengthened during H2:2024-25 (Chart 1.59 a). All the dimensions of the BSI, except profitability, improved during the period (Chart 1.59 b).  I.5 Non-Bank Financial Intermediaries (NBFIs) I.5.1 Global NBFIs 1.69 Over the last two decades, the non-bank financial sector has become an important provider of financial intermediation, and the assets of NBFIs have grown substantially relative to banks (Chart 1.60). According to the Financial Stability Board (FSB), of the estimated US$ 486.4 trillion global financial assets as at end-December 2023, the share of NBFIs rose to 49.1 per cent, growing at more than double the pace of banking sector46. 1.70 The rapid growth in the non-bank financial sector, however, has been accompanied by excessive use of leverage. Global hedge funds have significantly increased their use of synthetic leverage through derivatives over the past decade, which stands above 20 for multiple strategies (Chart 1.61). Similarly, asset managers, another prominent set of NBFIs, have also increased their leverage through long futures positions in the US treasury and equity markets to enhance their returns.  1.71 The recent market turmoil following April 2 tariff announcement, like previous market stress episodes such as the dash-for-cash episode of March 2020, has once again exposed risks posed by NBFIs globally due to their high leverage. Sudden shocks can trigger forced unwinding of leveraged positions, bringing to the fore hidden fragilities, and cause broader market disruptions47. 1.72 As the prominence of NBFIs in intermediation has grown globally, their growing interconnectedness and interdependence with the banking sector is a source of systemic risk (Chart 1.62 a and b). The growth of NBFIs has coincided with increasing asset-liability dependencies with banks48. Banks extend credit to or invest in NBFIs even as NBFIs rely on banks for their liquidity needs. Moreover, as banks and NBFIs adopt similar business models, the commonality of exposures of banks and NBFIs could amplify market stress49, especially if NBFIs resort to firesales as seen in the September 2022 pension fund crisis in the U.K. Thus, there are risks of both spillovers and spillbacks due to the growing bank- NBFI interconnectedness.   I.5.2 Domestic NBFIs 1.73 The bank-NBFI interconnectedness in India has also grown as the footprint of NBFIs increased over the years. However, prudent and proactive regulatory policies have ensured that the build-up of bank-NBFI connections remain contained (Chart 1.63 a and b).  1.74 The NBFC sector50 remains healthy with strong capital buffers, robust interest margins and earnings and low levels of impairment (Chart 1.64). Loan growth moderated as the effects of regulatory measures to increase risk weights on certain segments of consumer credit as well as on bank lending to NBFCs continued to weigh on their lending activities (Chart 1.65 a, b and c). The restoration of risk weights on bank lending and easing of financial conditions, however, are expected to improve credit prospects. 1.75 NBFCs, including housing finance companies (HFCs), and fintech51 firms account for 84.3 per cent of personal loans below ₹50,000 (Chart 1.66 a). Around 10 per cent of the borrowers availing a personal loan under ₹50,000 had an overdue personal loan. Moreover, a little over two-thirds of borrowers who have availed personal loan in the last quarter had more than three live loans at the time of origination (Chart 1.66 b).  1.76 Combined credit from NBFCs and NBFC-MFIs to the microfinance sector, which comprise 50.7 per cent of total credit outstanding to the sector, contracted by 14.5 per cent during 2024-25. Furthermore, the share of stressed assets of NBFCs (including NBFC-MFIs) increased from 3.9 per cent in September 2024 to 5.9 per cent in March 2025.   1.77 Slippage ratios have been trending upwards, especially in respect of upper layer NBFCs (Chart 1.67 a). Alongside, the write-offs are also growing (Chart 1.67 b). There are a few outlier NBFCs that have been registering sharper growth even as their write-offs remain high (Chart 1.67 c).  1.78 Despite decrease in bank lending to NBFCs, bank finance remains the dominant source of funding for NBFCs (Chart 1.68 a). The decline in borrowings from banks increased overall cost of funds (Chart 1.68 b). Many NBFCs have increased their foreign currency borrowings to diversify funding sources and manage their costs (Chart 1.68 c). Importantly, close to 80 per cent of these borrowings are hedged. 1.79 There has been a marginal deterioration in the non-banking stability indicator (NBSI)52 since the December 2024 FSR, as two of the five dimensions showed an increase in risk (Chart 1.69 a and b). 1.80 Overall, the NBFC sector remains resilient, and the sector is well positioned to support economic growth aided by healthy balance sheets. The sector, however, remains vulnerable to stress in household balance sheets with attendant consequences for asset quality (retail loan GNPA stood at 3.1 per cent compared to 1.2 per cent for banks in March 2025) and a rise in funding cost due to difficulty in diversifying funding sources, especially for lower-rated companies.   Mutual Funds 1.81 The assets under management (AUM) of the domestic mutual funds industry continued to grow and reached a record high of ₹72.2 lakh crore in May 2025 (Chart 1.70). Systematic investment plans (SIPs), on the other hand, saw some slowdown in recent months, both in terms of net contributions and accounts (Chart 1.71). The decline in accounts could be attributed to asset management companies (AMCs), pursuant to a SEBI directive, considering the failed SIPs53 as closed/cancelled from the month of January 2025.  1.82 Among different equity-oriented schemes, sectoral/thematic funds have attracted largest inflows over the last year and half, except in the last three months (Chart 1.72 a and b). In debt-oriented schemes, on the other hand, liquid and money market funds attracted more inflows during October 2024 to May 2025 (Chart 1.72 c).   I.6 Systemic Risk Survey (SRS) 1.83 According to the latest round of the Reserve Bank’s systemic risk survey (SRS) conducted in May 2025, all major risk groups remain in the ‘medium-risk’ category. Global and institutional risks were perceived to have increased compared with the previous survey round, whereas macroeconomic and financial market risks registered a marginal decline. At sub-category level, the risk perception of global growth and geopolitical conflict/ geoeconomic fragmentation recorded the most significant increase and were assessed as ‘high-risk’. Other major risks perceived to be in the ‘high-risk’ category include equity price volatility, climate risk and cyber risk. Overall, the survey respondents viewed geopolitical conflicts, capital outflows and reciprocal tariff/ trade slowdown as major near-term potential risks to financial stability (Chart 1.73). 1.84 Around two-thirds of the respondents expressed decreasing confidence in the stability of the global financial system. On the other hand, over 90 per cent of the participants expressed higher or similar confidence in the Indian financial system, with three-fourths expecting trade tension and protectionist policies to have moderate impact on India’s financial stability. Respondents assessed that export-dependent manufacturing sectors (e.g., textiles, readymade garments, electronics), MSMEs in export clusters and shipping and logistics industry would be the most affected by the global trade disruption.  1.85 About 80 per cent of the respondents perceived that the prospects of Indian banking sector have either improved or remain unchanged, underlining the resilience and strength of the sector. Almost 60 per cent of participants expected the asset quality of the banking sector to improve or remain unchanged in the following six months. Majority of the respondents perceived the trade slowdown to have a moderate to low impact on banking sector asset quality. 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. Detailed survey results are provided in Annex 1. 1 As per the OECD’s Economic Outlook Report, June 2025, the new tariffs introduced by the United States this year up to mid-May are estimated to have raised the (ex-ante) effective tariff rate on US merchandise imports to 15.4 per cent, from just over 2 per cent in 2024. 2 Gourinchas, Pierre-Olivier (2025), “The Global Economy Enters a New Era”, IMF Blog, April. 3 International Monetary Fund (2025), “World Economic Outlook: A Critical Juncture amid Policy Shifts”, April. 4 International Monetary Fund (2025), “Global Financial Stability Report: Enhancing Resilience amid Uncertainty”, April. 5 As per the World Bank GEP report, global trade growth is projected to decelerate to 1.8 per cent in 2025, a downward revision of 1.3 percentage points from the previous January 2025 projection. 6 Dabla-Norris, Era, Gaspar, Vitor, Poplawski-Ribeiro, Marcos (2025), “Rising Global Debt Requires Countries to Put their Fiscal House in Order”, IMF Blog, April. 7 Dabla-Norris, Era and Furceri, Davide (2025), “Debt is Higher and Rising Faster in 80 Per cent of Global Economy”, IMF Blog, May. 8 ARG: Argentina; AUS: Australia; BRA: Brazil; CAN: Canada; CHN: China; DEU: Germany; FRA: France; GBR: United Kingdom; IDN: Indonesia; IND: India; ITA: Italy; MEX: Mexico; MYS: Malaysia; PHL: Philippines; THA: Thailand; TUR: Republic of Türkiye; USA: United States; ZAF: South Africa. 9 AUS: Australia; BRA: Brazil; CAN: Canada; CHN: China; DEU: Germany; FRA: France; GBR: United Kingdom; IDN: Indonesia; IND: India; ITA: Italy; MEX: Mexico; MYS: Malaysia; PHL: Philippines; TUR: Turkey; USA: United States; ZAF: South Africa. 10 Adrian, Tobias, Nikolaou, Kleopatra, Wu, Jason (2025), “Fostering Core Government Bond Market Resilience, IMF Blog, May. 11 Growth outturn refers to the actual economic growth compared to what was originally forecast. 12 Reserve Bank of India (2025), “Monetary Policy Statement”, June. 13 Reserve Bank of India (2025), “Monetary Policy Report”, April. 14 Reserve Bank of India (2025), “Annual Report”, May. 16 Patra, Michael Debabrata, Behera, Harendra and Muduli, Silu (2022), “Capital Flows at Risk: India’s Experience”, RBI Bulletin, June. 17 Adrian, Tobias (2025), “Enhancing Financial Stability for Resilience During Uncertain Times”, IMF Blog, April. 18 The Federal Reserve Board (2025), “Financial Stability Report”, April. 19 Financial Stability Board (2022), “Liquidity in Core Government Bond Markets”, October. 20 Barth, Daniel, Kahn, R. Jay, and Mann, Robert (2023), “Recent Developments in Hedge Funds’ Treasury Futures and Repo Positions: is the Basis Trade Back?”, FEDS Notes, Washington: Board of Governors of the Federal Reserve System, August. 21 Kashyap, Anil K, Stein, Jeremy C., L. Wallen, Jonathan, and Younger, Joshua (2025), “Treasury Market Dysfunction and the Role of the Central Bank”, BPEA Conference Draft, March. 22 Iorio, Benjamin, Li, Dan, and Petrasek, Lubomir (2024), “Why Do Mutual Funds Invest in Treasury Futures?”, FEDS Notes, Washington: Board of Governors of the Federal Reserve System, May. 23 International Monetary Fund (2025), “Global Financial Stability Report: Enhancing Resilience amid Uncertainty”, April. 24 Shin, Hyun Song, Wooldridge, Philip and Xia, Dora (2025), “US dollar’s slide in April 2025: the role of FX hedging”, BIS Bulletin No. 105, June. 25 Bull steepening refers to a change in the yield curve caused by short-term interest rates falling faster than long-term rates, widening the spread between the two, that is, the term spread. 26 Semi-liquid securities have average of 1-10 trades (of face value>=₹5 crore) per day during previous calendar month. Illiquid securities have average of less than 1 trade (of face value>=₹5 crore) per day during previous calendar month. 28 According to the Institute of International Finance (IIF), foreign portfolio outflows from EMEs at ~US$ 40 billion in the December 2024 quarter were the highest since the pandemic (Q1:2020 - US$ 62.8 billion). 29 Institute of International Finance (2025), “Capital Flows Tracker”, February. 30 Fendoglu, Salih, Mahvash S. Qureshi, and Felix Suntheim (2025), “How Rising Geopolitical Risks Weigh on Asset Prices”, IMF Blog, April. 32 The interest coverage ratio is the ratio of earnings before interest and taxes (EBIT) to interest expenses. 33 The debt service ratio is defined as the ratio of interest payments plus amortisations to income. As such, the DSR provides a flow-to-flow comparison – the flow of debt service payments divided by the flow of income and therefore reflects the share of income used to service debt. 34 Cash buffers are defined as cash and cash equivalent assets as a percentage of total financial liabilities. 35 After adjusting for natural hedge. 36 Includes personal loans, credit cards, consumer durable loans and other personal loans. 37 In this analysis, consumer segment loans are used as a proxy for the total household debt and represents about 94 per cent of total household debt as at end-December 2024. Consumer segment loans refer to credit that is extended to individuals in their personal capacity, utilised for either personal or business purposes, and is recorded in the consumer repository of credit bureau(s). 38 Debt outstanding divided by number of live unique borrowers at the end of each period. 39 The segregation of risk tiers based on CIBIL scores is as follows - Super-Prime:791-900; Prime Plus: 771-790, Prime: 731-770; Near Prime: 681-730; and Sub-Prime: 300-680. 40 Prakash, Anupam, S, Suraj, Thakur, Ishu and Priyadarshini, Mousumi (2024), “Estimating the Financial Wealth of Indian Households”, RBI Bulletin, July. 41 The analyses done in this section are based on domestic operations of SCBs (including SFBs), unless otherwise stated. 42 Special mention account (SMA) is defined as: a) For loans with revolving facilities (e.g. cash credit/ overdraft): if outstanding balance remains continuously more than the sanctioned limit or drawing power, whichever is lower, for a period of 31-60 days - SMA-1; 61-90 days - SMA-2. b) For loans other than revolving facilities: if principal or interest payment or any other amount wholly or partly overdue remains outstanding up to 30 days - SMA-0; 31-60 days - SMA-1; 61-90 days - SMA-2. 43 Credit impulse is the change in new credit issued as a percentage of GDP. Essentially, it captures the change in credit between time t and (t-1), and between (t-1) and (t-2), as a percentage of four-period rolling average of quarterly GDP at time (t-1). 44 RBI circular no. RBI/2024-25/120 DOR.STR.REC.61/21.06.001/2024-25 dated February 25, 2025, on “Exposures of Scheduled Commercial Banks (SCBs) to Non-Banking Financial Companies (NBFCs) – Review of Risk Weights”. 45 Including small finance banks (SFBs). 46 FSB (2024), “Global Monitoring Report on Non-Bank Financial Intermediation 2024”, December. 47 International Monetary Fund (2025), “Global Financial Stability Report: Enhancing Resilience amid Uncertainty”, April. 48 Acharya, Viral V., Cetorelli, Nicola and Tuckman, Bruce (2024), “Where do Banks End and NBFIs Begin?”, NBER Working Paper 32316, April. 49 Cetorelli, Nicola, Landoni, Mattia, and Lu, Lina (2023), “Non-Bank Financial Institutions and Banks’ Fire-Sale Vulnerabilities”, Federal Reserve Bank of New York Staff Reports, No. 1057, March. 50 The analyses done in this section are based on NBFCs in upper and middle layers but excludes housing finance companies (HFCs), core investment companies (CICs) and standalone primary dealers (SPDs), but includes NBFCs presently under resolution; data based on provisional data available as of June 10, 2025. 51 The methodology for classifying NBFCs as Fintech is based on TransUnion CIBIL’s market knowledge that they have a digital first approach for its lending business and/or are members of industry bodies like FACE, UFF and IAMAI. 52 See Annex 2 for detailed methodology and variables used. 53 The failed SIPs mean SIPs where 3 consecutive instalments with respect to daily, weekly, fortnightly, and monthly intervals and 2 consecutive instalments with respect to bi-monthly, quarterly or longer intervals have failed. |
Overview
|
The Financial Stability Report (FSR) is a half-yearly publication, with contributions from all financial sector regulators. It presents the collective assessment of the Sub Committee of the Financial Stability and Development Council on current and emerging risks to the stability of the Indian financial system. Global Macrofinancial Risks Elevated economic and trade policy uncertainties are testing the resilience of the global economy and the financial system. Multilateral agencies have downgraded global growth forecasts largely reflecting trade disruptions and heightened volatility. Financial markets remain volatile, especially core government bond markets, driven by shifting policy and geopolitical environment. Alongside, existing vulnerabilities such as soaring public debt levels, excessive risk taking in the non-banking financial sector1 and elevated asset valuations have the potential to amplify fresh shocks. As countries confront varying trade-offs between growth and inflation, monetary authorities are charting divergent policy trajectory. Emerging market economies face significant challenges from headwinds emanating from escalating trade tensions, prolonged and intensified geopolitical tensions, and spillovers from advanced economies. Domestic Macrofinancial Risks Despite an uncertain and challenging global economic backdrop, the Indian economy remains a key driver of global growth, underpinned by sound macroeconomic fundamentals and prudent macroeconomic policies. Since India’s growth is mainly driven by buoyant domestic demand, it remains relatively insulated from the global headwinds. The Indian economy continues to grow at a healthy pace, which coupled with steadily moderating inflation, is aiding macroeconomic and financial stability. The domestic financial system is exhibiting resilience fortified by healthy balance sheets of banks and non-banks. Financial conditions have eased supported by accommodative monetary policy and low volatility in financial markets. The strength of the corporate balance sheets also lends support to overall macroeconomic stability. While the economy and the financial system are relatively well positioned to withstand tariff-induced shocks, risks from global spillovers and escalation in geopolitical conflicts remain a key concern. Financial Institutions: Soundness and Resilience The soundness and resilience of scheduled commercial banks (SCBs) are bolstered by robust capital buffers, multi-decadal low non-performing loans and strong earnings. Furthermore, macro stress test results showed that SCBs’ aggregate capital levels will continue to remain above the regulatory minimum even under adverse stress scenarios. The capital position of the urban cooperative banks (UCBs) strengthened, while that of the non-banking financial companies (NBFCs) remained well above the regulatory minimum. The consolidated solvency ratio of the insurance sector, both life and non-life segments, remained above the minimum prescribed threshold limit. Stress test results of mutual funds and clearing corporations affirm their resilience to shocks. Regulatory Initiatives and Other Developments in the Financial Sector Globally, financial sector regulators in most major economies have implemented measures to strengthen the financial system by bringing key reforms in liquidity management, credit risk regulation and securitisation practices. Furthermore, they are stepping up efforts to safeguard the financial network against cyberattacks and technological failures by enhancing surveillance mechanisms and establishing standardised incident-reporting frameworks. Regulators continue to assess climate-related risks to the financial system by developing standards to integrate climate objectives into broader financial stability assessments. Domestic regulators are actively implementing a series of regulatory reforms aimed at enhancing the stability, transparency and inclusiveness of the financial system in line with global best practices. These initiatives focus on combating financial and digital fraud, promoting liquidity resilience, regulating digital lending and safeguarding retail investors. Assessment of Systemic Risk According to the latest round of the Reserve Bank’s systemic risk survey (SRS) conducted in May 2025, all major risk groups remain in the ‘medium risk’ category. Respondents remained optimistic about the soundness of the domestic financial system, with 92 per cent expressing higher or similar level of confidence in the Indian financial system. Around two-thirds of the respondents expressed decreasing confidence in the stability of the global financial system. Geopolitical conflicts, capital outflows and reciprocal tariff/trade slowdown were identified as major near-term risks to domestic financial stability. 1 International Monetary Fund (2025), “Global Financial Stability Report: Enhancing Resilience amid Uncertainty”, April. |
List of Select Abbreviations
| 3-MMA | 3-Month Moving Average |
| AA | Adjudicating Authority |
| AEs | Advanced Economies |
| AFA | Authorisation for Assignment |
| AFS | Available for Sale |
| AID | All Inclusive Directions |
| AIFs | Alternative Investment Funds |
| AIFIs | All-India Financial Institutions |
| AMCs | Asset Management Companies |
| AMFI | Association of Mutual Funds in India |
| APIs | Application Programming Interfaces |
| APY | Atal Pension Yojana |
| ARCL | AMC Repo Clearing Limited |
| ARCs | Asset Reconstruction Companies |
| ASPs | Annuity Service Providers |
| AUM | Assets Under Management |
| BCBS | Basel Committee on Banking Supervision |
| BIFR | Board for Industrial and Financial Reconstruction |
| Bima-ASBA | Bima Applications Supported by Blocked Amount |
| BIS | Bank for International Settlements |
| BLC | Balanced Life Cycle Fund |
| BPS | Basis Points |
| BRSR | Business Responsibility and Sustainability Reporting |
| BSI | Banking Stability Indicator |
| CAD | Current Account Deficit |
| CAGR | Compounded Annual Growth Rate |
| CART | Continuous Assessment-Based Red Teaming |
| CASA | Current Account and Savings Account |
| CCIL | Clearing Corporation of India Ltd. |
| CCPs | Central Counterparties |
| CCRI | Credit Concentration Risk Index |
| CCs | Clearing Corporations |
| CDs | Certificates of Deposit |
| CDSL | Central Depository Services Limited |
| CET1 | Common Equity Tier 1 |
| CGFMU | Credit Guarantee Fund for Micro Units |
| CICs | Core Investment Companies |
| CIRP | Corporate Insolvency Resolution Process |
| CLOs | Collateralised Loan Obligations |
| CMs | Clearing Members |
| CoC | Committee of Creditors |
| CP | Commercial Paper |
| CPGRAMS | Centralised Public Grievance Redress and Monitoring System |
| CPI | Consumer Price Index |
| CRAR | Capital to Risk-Weighted Assets Ratio |
| CRAs | Credit Rating Agencies |
| CRR | Cash Reserve Ratio |
| D-SIIs | Domestic Systemically Important Insurers |
| DGA | Duration Gap Analysis |
| DICGC | Deposit Insurance and Credit Guarantee Corporation |
| DIIs | Domestic Institutional Investors |
| DFM | Dynamic Factor Model |
| DIF | Deposit Insurance Fund |
| DLAs | Digital Lending Apps |
| DPD | Days Past Due |
| DRS | Debt Relief Schemes |
| DSR | Debt Service Ratio |
| EAR | Earnings At Risk |
| EBIT | Earnings Before Interest and Taxes |
| EBLR | External Benchmark-Based Lending Rate |
| EBPT | Earnings Before Profit and Tax |
| ECB | External Commercial Borrowings |
| ECLGS | Emergency Credit Line Guarantee Scheme |
| EMDEs | Emerging Markets and Developing Economies |
| EMEs | Emerging Market Economies |
| EMP | Exchange Market Pressure |
| EoDB | Ease of Doing Business |
| EOI | Expression of Interest |
| EPS | Earnings per Share |
| ERPs | ESG Rating Providers |
| ESG | Environmental, Social, and Governance |
| ETFs | Exchange Traded Funds |
| FBs | Foreign Banks |
| FCI | Financial Conditions Index |
| FDI | Foreign Direct Investment |
| FEMA | Foreign Exchange Management Act, 1999 |
| FIRE | Format for Incident Reporting Exchange |
| FMEs | Fund Management Entities |
| FoFs | Fund of Funds |
| FPI | Foreign Portfolio Investment |
| FSB | Financial Stability Board |
| FSDC | Financial Stability and Development Council |
| FSR | Financial Stability Report |
| FSSI | Financial System Stress Indicator |
| FVTPL | Fair Value Through Profit and Loss |
| FY | Financial Year |
| GAOs | Global Administrative Offices |
| GaR | Growth-at-Risk |
| G20 | Group of Twenty |
| GDP | Gross Domestic Product |
| GEP | Global Economic Prospects |
| GNPA | Gross Non-Performing Asset |
| GPR | Global Geopolitical Risk |
| GSIBs | Globally Systemically Important Banks |
| G-Secs | Government Securities |
| GST | Goods and Services Tax |
| HFCs | Housing Finance Companies |
| HHI | Herfindahl-Hirschman index |
| HFT | Held for Trading |
| HQLAs | High Quality Liquid Assets |
| HTM | Held to Maturity |
| HVDLEs | High Value Debt Listed Entities |
| HySAC | Hybrid Securities Advisory Committee |
| IAIS | International Association of Insurance Supervisors |
| IBBI | Insolvency and Bankruptcy Board of India |
| ICR | Interest Coverage Ratio |
| IESSA | International Ethics Standards for Sustainability Assurance |
| IFSC | International Financial Services Centre |
| IFSCA | International Financial Services Centres Authority |
| IIBX | India International Bullion Exchange |
| IIF | Institute of International Finance |
| IIP | International Investment Position |
| IM | Information Memorandum |
| IMF | International Monetary Fund |
| INR | Indian Rupee |
| InvITs | Infrastructure Investment Trusts |
| IOSCO | International Organization of Securities Commission |
| IPO | Initial Public Offerings |
| IPs | Insolvency Professionals |
| IR | Information Ratio |
| IRDAI | Insurance Regulatory and Development Authority of India |
| ISPOT | Integrated SEBI Portal for Technical Glitches |
| ISSA | International Standard on Sustainability Assurance |
| IU | Information Utilities |
| KMPs | Key Managerial Personnel |
| KYC | Know Your Customer |
| LCR | Liquidity Coverage Ratio |
| LGD | Loss Given Default |
| LODR | Listing Obligations and Disclosure Requirements |
| LPCC | Limited Purpose Clearing Corporation |
| LSPs | Lending Service Providers |
| LT | Long-term |
| LTV | Loan-to-Value |
| MDG | Modified Duration Gap |
| MeitY | Ministry of Electronics and Information Technology |
| MF | Mutual Fund |
| MF Lite | Mutual Funds Lite |
| MII | Market Infrastructure Institutions |
| MITRA | Mutual Fund Investment Tracing and Retrieval Assistant |
| MNRL | Mobile Number Revocation List |
| MoU | Memorandum of Understanding |
| MRC | Minimum Required Corpus |
| MSME | Micro, Small And Medium Enterprises |
| MTM | Mark-To-Market |
| MVE | Market Value of Equity |
| NABARD | National Bank for Agriculture and Rural Development |
| NAVs | Net Asset Values |
| NaBFID | National Bank for Financing Infrastructure and Development |
| NBFCs | Non-Banking Financial Companies |
| NBFC-ML | Middle layer NBFCs |
| NBFC-UL | Upper layer NBFCs |
| NBFI | Non-Bank Financial Intermediaries |
| NBSI | Non-Banking Stability Indicator |
| NDS-OM | Negotiated Dealing System – Order Matching |
| NDTL | Net Demand and Time Liabilities |
| NeGD | National e-Governance Division |
| NFCs | Non-Financial Corporates |
| NFO | New Fund Offer |
| NHB | National Housing Bank |
| NIC | National Industrial Classification |
| NII | Net Interest Income |
| NIM | Net Interest Margin |
| NNPA | Net Non-Performing Assets |
| NOC | No-Objection Certificate |
| NOI | Net Operating Income |
| NPL | Non-Performing Loans |
| NPS | National Pension System |
| NRC | Nomination and Remuneration Committee |
| NSDL | National Securities Depository Limited |
| NSE IX | NSE International Exchange |
| NSFR | Net Stable Funding Ratio |
| NSO | National Statistical Office |
| NSUCBs | Non-Scheduled Urban Cooperative Banks |
| ODIs | Offshore Derivative Instruments |
| OECD | Organisation for Economic Co-operation and Development |
| OFIs | Other Financial Intermediaries |
| OIS | Overnight Indexed Swap |
| OOI | Other Operating Income |
| ORBIO | Offices of the Reserve Bank of India Ombudsman |
| OTC | Over-the-Counter |
| OVD | Officially Valid Document |
| P/E | Price-to-Earnings |
| PaRRVA | Past Risk and Return Verification Agency |
| PAT | Profit After Tax |
| PCE | Personal Consumption Expenditures |
| PCR | Provisioning Coverage Ratio |
| PDs | Primary Dealers |
| PFRDA | Pension Fund Regulatory and Development Authority |
| PL | Performing Loans |
| PSBs | Public Sector Banks |
| PSL | Priority Sector Lending |
| PVBs | Private Sector Banks |
| RAR | Risk Adjusted Return |
| RAs | Retirement Advisers |
| RBI | Reserve Bank of India |
| REIT | Real Estate Investment Trust |
| REER | Real Effective Exchange Rate |
| REs | Regulated Entities |
| RMBS | Residential Mortgage-Backed Securities |
| RMC | Risk Management Committee |
| RoA | Return on Asset |
| RoE | Return on Equity |
| RPs | Resolution Plans |
| RPTs | Related Party Transactions |
| RRBs | Regional Rural Banks |
| RSA | Rate-Sensitive Assets |
| RSL | Rate-Sensitive Liabilities |
| RWA | Risk-Weighted Assets |
| SBs | Stock Brokers |
| SBU | Separate Business Unit |
| SCBs | Scheduled Commercial Banks |
| SD | Standard Deviation |
| SDI | Securitised Debt Instrument |
| SDLs | State Development Loans |
| SEBI | Securities and Exchange Board of India |
| SFBs | Small Finance Banks |
| SGF | Settlement Guarantee Fund |
| SIF | Specialized Investment Funds |
| SIMM | Standard Initial Margin Model |
| SIPs | Systematic Investment Plans |
| SLR | Statutory Liquidity Ratio |
| SMAs | Special Mention Accounts |
| SM REIT | Small and Medium Real Estate Investment Trust |
| SOC | Security Operations Centre |
| SPDs | Stand-alone Primary Dealers |
| SRC | Stakeholder Relationship Committee |
| SRS | Systemic Risk Survey |
| SRVA | Special Rupee Vostro Account |
| ST | Short-term |
| SUCBs | Scheduled Urban Cooperative Banks |
| TGA | Traditional Gap Analysis |
| TMs | Trading Members |
| UCB | Urban Cooperative Bank |
| UPS | Unified Pension Scheme |
| USD | US Dollar |
| VARX | Vector Auto Regression with Exogenous Variables |
| VIX | Volatility Index |
Contents
| Foreword |
| List of Select Abbreviations |
| Overview |
| Chapter I : Macrofinancial Risks |
| Macroeconomic Outlook |
| Global Outlook |
| Domestic Outlook |
| Financial Markets |
| Global Financial Markets |
| Domestic Financial Markets |
| Corporate and Household Sector |
| Corporate Sector |
| Household Sector |
| Banking System |
| Non-Bank Financial Intermediaries (NBFIs) |
| Global NBFIs |
| Domestic NBFIs |
| Systemic Risk Survey (SRS) |
| Chapter II : Financial Institutions: Soundness and Resilience |
| Scheduled Commercial Banks (SCBs) |
| Asset Quality |
| Sectoral Asset Quality |
| Credit Quality of Large Borrowers |
| Capital Adequacy |
| Earnings and Profitability |
| Liquidity |
| Resilience – Macro Stress Tests |
| Sensitivity Analysis |
| Sensitivity Analysis of Small Finance Banks |
| Bottom-up Stress Tests: Derivatives Portfolio |
| Bottom-up Stress Tests: Credit, Market and Liquidity Risk |
| Primary (Urban) Cooperative Banks (UCBs) |
| Stress Testing |
| Non-Banking Financial Companies (NBFCs) |
| Stress Test - Credit Risk |
| Stress Test - Liquidity Risk |
| Stress Testing of Mutual Funds |
| Stress Testing Analysis at Clearing Corporations |
| Insurance Sector |
| Interconnectedness |
| Financial System Network |
| Contagion Analysis |
| Chapter III : Regulatory Initiatives in the Financial Sector |
| Global Regulatory Developments |
| Banking |
| Financial Markets |
| Cyber Resilience |
| Climate Finance |
| Initiatives from Domestic Regulators / Authorities |
| Use of Indian Rupee for Cross Border Settlements |
| Prevention of Financial and Digital Payments Fraud |
| Reserve Bank of India (Project Finance) Directions, 2025 |
| Amendments to Liquidity Coverage Ratio (LCR) Framework |
| Reserve Bank of India (Digital Lending Directions), 2025 |
| Reserve Bank of India (Forward Contracts in Government Securities) Directions, 2025 |
| Introduction of Mutual Funds Lite (MF Lite) Framework |
| Introduction of Specialised Investment Funds |
| Safer Participation of Retail Investors in Algorithmic Trading |
| Identifying Unclaimed Assets |
| System Audit of Stock Brokers (SBs) through Technology-based Measures |
| Access to Negotiated Dealing System – Order Matching (NDS-OM) |
| Intraday Monitoring of Position Limits for Index Derivatives |
| Operational Resilience of Financial Market Intermediaries |
| Changes to Disclosure Requirements |
| Other Developments |
| Customer Protection |
| Enforcement |
| Deposit Insurance |
| Corporate Insolvency Resolution Process (CIRP) |
| Developments in International Financial Services Centre (IFSC) |
| Pension Funds |
| Insurance |
| Annex 1 : Systemic Risk Survey |
| Annex 2 : Methodologies |
| Annex 3 : Important Domestic Regulatory Measures |
| Reserve Bank of India (RBI) |
| Securities and Exchange Board of India (SEBI) |
| Insurance Regulatory and Development Authority of India (IRDAI) |
| Pension Fund Regulatory and Development Authority (PFRDA) |
| Insolvency and Bankruptcy Board of India (IBBI) |
| International Financial Services Centres Authority (IFSCA) |
| LIST OF BOXES |
| Chapter I |
| 1.1 Tracing Market Reactions to Geopolitical Events: An Event Study Framework |
| Chapter II |
| 2.1 System-wide Concentration Risk from Large Borrowers |
| LIST OF CHARTS |
| Chapter I |
| 1.1 Global Uncertainty |
| 1.2 Financial System Stress Indicator |
| 1.3 Growth-Inflation Dynamics vis-à-vis Historical Average |
| 1.4 Global Growth Projections |
| 1.5 Inflation and Monetary Policy Actions – Major AEs and EMEs |
| 1.6 Public Debt – Global, AEs and EMEs |
| 1.7 Public Debt and Primary Balance – Country Comparison |
| 1.8 Change in Debt and Interest Expenses – Select AEs and EMEs |
| 1.9 Interest Rate – Growth Rate Differential (Real) – US and Europe |
| 1.10 Economic Growth |
| 1.11 Inflation - India |
| 1.12 India’s Fiscal Position Comparison - 2024 |
| 1.13 Debt-to-GDP and Interest Rate – Growth Rate Differential |
| 1.14 India’s Balance of Payments |
| 1.15 External Vulnerability Indicators and Foreign Exchange Reserves |
| 1.16 Asset Price Movements, Financial Conditions and Volatility |
| 1.17 Equity Market Valuation |
| 1.18 Bond Market Liquidity and Volatility |
| 1.19 Net Treasury Futures Positions by Entity Type, Swap Spreads and Hedge Funds Borrowing in US |
| 1.20 US Dollar Performance |
| 1.21 Domestic Financial Conditions |
| 1.22 Money Market Trends |
| 1.23 Government Bond Market |
| 1.24 Movement in USD/INR Exchange Rate |
| 1.25 Exchange Rate Indicators |
| 1.26 Equity Market Performance and Volatility |
| 1.27 Fund Flows and NSE Listed Companies Ownership Pattern |
| 1.28 Equity Valuations |
| 1.29 Share of Stocks with P/E Ratio above Respective Benchmarks |
| 1.30 Ownership Pattern in Nifty Stocks – FPIs, Individuals and Mutual Funds |
| 1.31 Impact of Geopolitical Risk on Financial Market Variables |
| 1.32 Corporate Bond Market Trends |
| 1.33 Monthly Trading Volumes for ARCL |
| 1.34 Sales and Profits - Listed Private NFCs |
| 1.35 Sector-wise Trend in ICR |
| 1.36 Debt-Service Ratio and Cash Buffers |
| 1.37 Corporate Sector Vulnerability Indicators |
| 1.38 Household Debt |
| 1.39 Household Borrowings |
| 1.40 Housing Loans Trends |
| 1.41 Per Capita Debt of Individual Borrowers |
| 1.42 Household-Individual Borrowings from Financial Institutions |
| 1.43 Household Financial Wealth |
| 1.44 Performance of SCBs |
| 1.45 SCBs’ Capital, Asset Quality and Profitability |
| 1.46 Profitability, Credit Growth and Deposit Profile |
| 1.47 Bank Loan Growth |
| 1.48 Asset Quality of Unsecured Retail Loans |
| 1.49 Slippages and Write-offs - Unsecured Retail Loans |
| 1.50 Bank Credit to the MSME Sector |
| 1.51 Asset Quality of Bank Credit to the MSME Sector |
| 1.52 Share of Credit to MSME Sector by Risk Tiers (By Amount Outstanding) |
| 1.53 NPA Ratio of Credit Extended under Select Guarantee Schemes |
| 1.54 Loan Growth in Consumer Segment |
| 1.55 Consumer Segment Asset Quality |
| 1.56 Share of Borrowers by Risk Tier in Consumer Segment |
| 1.57 Credit to the Microfinance Sector |
| 1.58 Stressed Assets and Indebtedness in the Microfinance Sector |
| 1.59 Banking Stability Indicator and Map |
| 1.60 Global NBFI Share |
| 1.61 Hedge Funds’ Synthetic Leverage by Strategy |
| 1.62 Bank-NBFI Interconnectedness |
| 1.63 Bank-NBFI Interconnectedness in India |
| 1.64 NBFC Sector – Key Financial Parameters |
| 1.65 NBFC Credit and Bank Lending to NBFCs |
| 1.66 Personal Loans – Lenders’ Share and Loan Origination |
| 1.67 Slippage Ratio, Write-Offs and Outlier NBFCs |
| 1.68 NBFCs (UL+ML) Borrowing and Funding Profile |
| 1.69 Non-Banking Stability Indicator and Map |
| 1.70 Trends in the AUM of the B30 and T30 Cities of the Domestic Mutual Fund Industry |
| 1.71 Trends in Monthly SIP Contributions and Outstanding SIP Accounts |
| 1.72 Inflows in Open-ended Mutual Fund Schemes |
| 1.73 Potential Risks to Financial Stability |
| Chapter II |
| 2.1 Deposit and Credit Profile of SCBs |
| 2.2 Select Asset Quality Indicators |
| 2.3 Sectoral Asset Quality Indicators |
| 2.4 Select Asset Quality Indicators of Large Borrowers |
| 2.5 Capital Adequacy |
| 2.6 Select Performance Indicators of SCBs |
| 2.7 Liquidity Ratios |
| 2.8 Macro Scenario Assumptions |
| 2.9 CRAR Projections under Stress Scenarios |
| 2.10 Projection of CET1 Capital Ratio under Stress Scenarios |
| 2.11 Projection of GNPA Ratio under Stress Scenarios |
| 2.12 Credit Risk – Shocks and Outcomes |
| 2.13 Credit Concentration Risk: Individual Borrowers – Exposure |
| 2.14 Credit Concentration Risk: Group Borrowers – Exposure |
| 2.15 Credit Concentration Risk: Individual Borrowers – Stressed Advances |
| 2.16 AFS and FVTPL (including HFT) Portfolios: Bank-group wise |
| 2.17 HTM Portfolio – Composition |
| 2.18 HTM Portfolio – Unrealised Gain/ Loss as on March 31, 2025 |
| 2.19 Equity Price Risk |
| 2.20 LCR-based Liquidity Stress Test |
| 2.21 MTM position of Total Derivatives Portfolio of Select Banks – March 2025 |
| 2.22 MTM Impact of Shocks on Derivatives Portfolio of Select Banks |
| 2.23 Income from the Derivatives Portfolio |
| 2.24 Credit and Market Risks |
| 2.25 Liquidity Risk - Liquid Assets Ratio |
| 2.26 Credit Profile and Asset Quality Indicators of UCBs |
| 2.27 Stress Test of UCBs |
| 2.28 Credit Profile of NBFCs |
| 2.29 Growth and Delinquency of Components of Retail Loans |
| 2.30 Asset Quality of NBFCs |
| 2.31 Capital Adequacy and Profitability |
| 2.32 Liquidity Stock Measures |
| 2.33 Credit Risk in NBFCs - System Level |
| 2.34 Range (Surplus (+)/ Deficit (-)) of LR-RaR and LR-CRaR Maintained by AMCs over AMFI Prescribed Limits |
| 2.35 Bilateral Exposures between Entities in the Financial System |
| 2.36 Instrument-wise Exposure among Entities in the Financial System |
| 2.37 Network Plot of the Financial System - March 2025 |
| 2.38 Net Receivables (+ve)/ Payables (-ve) by Institutions |
| 2.39 Inter-Bank Market |
| 2.40 Contribution of Different Bank Groups in the Inter-Bank Market |
| 2.41 Composition of Fund based Inter-Bank Market |
| 2.42 Network Structure of the Indian Banking System (SCBs + SUCBs) – March 2025 |
| 2.43 Connectivity Statistics of the Banking System (SCBs) |
| 2.44 Gross Receivables of AMC-MFs from the Financial System |
| 2.45 Gross Receivables of Insurance Companies from the Financial System |
| 2.46 Gross Payables of NBFCs to the Financial System |
| 2.47 Gross Payables of HFCs to the Financial System |
| 2.48 Gross Payables/Receivables of AIFIs to/from the Financial System |
| Chapter III |
| 3.1 Summary of Outcomes - Resolution to Liquidation Ratio |
| 3.2 NPS and APY – Subscribers and AUM Trend |
| 3.3 NPS and APY AUM: Asset Class-wise Bifurcation |
| LIST OF TABLES |
| Chapter I |
| 1.1 Capital Flows |
| 1.2 Resource Mobilisation through the Indian Capital Markets |
| 1.3 Share of Floating Rate Loans - Overall |
| 1.4 Distribution of Retail Loans by Interest Rate Framework |
| Chapter II |
| 2.1 Decline in System Level CRAR |
| 2.2 PV01 of AFS and FVTPL (including HFT) Portfolios |
| 2.3 Interest Rate Risk – Bank-groups - Shocks and Impacts |
| 2.4 Other Operating Income – Profit / (Loss) on Securities Trading |
| 2.5 Earnings at Risk (EAR) - Traditional Gap Analysis (TGA) |
| 2.6 Market Value of Equity (MVE) - Duration Gap Analysis (DGA) |
| 2.7 NBFCs’ Sources of Funds |
| 2.8 Liquidity Risk in NBFCs |
| 2.9 Stress Testing of Open-Ended Debt Schemes of Mutual Funds – Summary Findings – April 2025 |
| 2.10 Summary of Stress Tests and Liquidity Analysis of MF Midcap and Smallcap Schemes |
| 2.11 Minimum Required Corpus of Core SGF Based on Stress Testing Analysis at Clearing Corporations |
| 2.12 Solvency Ratio of Insurance Sector |
| 2.13 Simulated Contagion Losses due to Hypothetical Bank Failure – March 2025 |
| 2.14 Simulated Contagion Losses due to Hypothetical NBFC Failure – March 2025 |
| 2.15 Simulated Contagion Losses due to Hypothetical HFC Failure – March 2025 |
| Chapter III |
| 3.1 Category of Complaints Received under the RB-IOS, 2021 |
| 3.2 Type/Category of Complaints |
| 3.3 Status of Disputes on SmartODR.in |
| 3.4 Coverage of Deposits |
| 3.5 Bank Group-wise Deposit Protection Coverage |
| 3.6 Deposit Insurance Premium |
| 3.7 Deposit Insurance Fund and Reserve Ratio |
| 3.8 Status of Corporate Insolvency Resolution Process |
| 3.9 Sectoral Distribution of CIRPs |
| 3.10 Outcome of CIRPs, Initiated Stakeholder-wise |
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