RBI Data Analysis

Job Summary

Credit Risk in Indian Banking: RBI Data Analysis

For most of the last decade, credit risk in Indian banking meant a crisis story. Ballooning bad loans. Undercapitalised public sector banks. A system searching for a floor. That story has changed. This analysis of Indian banking credit risk data draws straight from RBI s own numbers. It shows a sector operating at some of the lowest stressed-asset levels in its history. At the same time, new risks are quietly building underneath: unsecured retail credit, gold loans, and a fast-growing NBFC sector. So for anyone building credit risk models on Indian data, the headline ratio matters less than the underlying trend. What matters is understanding where the improvement came from, and where the residual risk now sits.

This piece works through what RBI s own publications actually show. Specifically, we draw on RBI s Financial Stability Report (FSR) and its Report on Trend and Progress of Banking in India. We also use RBI s supervisory data releases directly. First, we break the picture down by bank category. Then we connect it to how analysts actually model default risk in practice.

What Indian Banking Credit Risk Data Shows About Asset Quality

How far asset quality has improved

The single most-cited number here is the Gross Non-Performing Asset (GNPA) ratio of Scheduled Commercial Banks (SCBs). RBI s Financial Stability Report was released in June 2026. It put the GNPA ratio of SCBs at 1.8% as of March 2026 a multi-decadal low. RBI s own baseline projection expects only a slight edge-up, to around 1.9% by March 2028. The central bank called this a modest, manageable increase, not a reversal of the underlying trend.

That figure looks even more striking against where the system stood less than a decade ago. In 2018, RBI s Financial Stability Report flagged a system-wide GNPA ratio of 11.46%. Banks under Prompt Corrective Action (PCA) RBI s restrictive framework for weak lenders ran even higher. The distance between 11.46% in 2018 and 1.8% in 2026 is the real story here. In fact, it represents one of the fastest deleveraging cycles any major banking system has gone through. And it wasn t accidental. It followed a specific, traceable sequence of interventions. Any credit risk analyst working with this data should be able to name them.

Capital buffers moved in the same direction. RBI s June 2026 FSR put the system-wide Capital to Risk-Weighted Assets Ratio (CRAR) at 17.7%. The Common Equity Tier-1 (CET1) ratio stood at 15.3%. Both are multi-decade highs, per RBI. Similarly, RBI s Report on Trend and Progress of Banking in India 2024-25 shows the same trend over a longer horizon. System CRAR rose from 12.94% in March 2015 to roughly 17.4% by end-March 2025. Profitability tracked the same arc. Scheduled commercial banks aggregate net profit rose too from 2.63 lakh crore in FY2022-23 to 4.01 lakh crore in FY2024-25, per RBI s Trend and Progress data.

What stress tests add to the Indian banking credit risk picture

RBI s stress testing adds another layer here, separate from the point-in-time ratios above. Under RBI s macro stress test scenarios in the June 2026 FSR, the banking system absorbed hypothetical adverse shocks. Even so, aggregate capital ratios stayed comfortably above regulatory minimums. Compare that to RBI s own 2018 FSR. That report projected a comparable stress scenario could push GNPA at PCA banks as high as 22.3%. It also warned that at least six banks faced a capital shortfall. The gap between those two outcomes, eight years apart, is telling. It may say more about systemic resilience than either year s baseline GNPA ratio alone.

None of this means credit risk has disappeared, however. RBI s June 2026 FSR was explicit on this point: new vulnerabilities are building even as legacy NPAs shrink. Specifically, RBI surveyed banks and NBFCs for the report. They flagged rising household debt, rapid growth in gold loans, and AI-enabled cyber threats as their top risks. In other words, the credit risk profile of Indian banking hasn t shrunk so much as shifted. It has moved from the corporate and industrial exposures that dominated the 2PHONE_NUMBER cycle, toward retail and unsecured lending instead.

Indian Banking Credit Risk Data by Bank Category and Sector

PSBs, private banks, and foreign banks compared

Aggregate numbers hide where the improvement actually concentrated, though. RBI supervisory data as of breaks the 2.15% domestic GNPA ratio down by bank category. The Ministry of Finance cited this data in a Parliament reply. Public Sector Banks (PSBs) stood at 2.50%. Private Sector Banks (PVBs) stood at 1.73%. Foreign Banks stood at just 0.80%. Interestingly, the same data shows PSBs improving faster than private or foreign banks since March 2018. In other words, the segment that was historically the weakest link has also recovered the fastest. Even so, it still carries the highest absolute ratio of the three.

That PSB recovery shows up in profitability too, per RBI s Trend and Progress report. PSBs aggregate net profit rose from 1.05 lakh crore in FY2022-23 to 1.78 lakh crore in FY2024-25. That s a faster proportional gain than the system as a whole. It s consistent with a segment recovering from a lower base.

Where credit concentrates: NBFCs, MSMEs, and smaller lenders

Beyond the bank-vs-bank split, this Indian banking credit risk data also shows where credit itself concentrates. NBFC credit is now systemically significant. It reached 14.6% of GDP in FY25, up from 13.5% a year earlier. Industry and retail exposures made up a combined 81.1% share of that NBFC credit as of end-March 2025. Services accounted for the remaining 15.4%. On the bank side, credit to MSMEs grew faster than credit to large enterprises over the same period. Meanwhile, Small Finance Banks (SFBs) posted strong balance-sheet growth. Even so, their asset quality worsened year-on-year, even as their credit-deposit ratio improved to 86.4%. That divergence is telling: large banks and PSBs improving, SFBs under pressure. It s exactly the kind of segment-level signal a system-wide GNPA number alone won t surface.

Smaller institutions tell a related story, too. The number of Urban Co-operative Banks (UCBs) fell by 15 during FY25, to 1,457, as consolidation continued in that segment. Still, UCB credit growth accelerated to 6.7% the fastest pace in six years. Fraud data adds a further wrinkle here. Private sector banks accounted for 59.3% of reported fraud cases by number in FY25. Public sector banks, meanwhile, accounted for 70.7% of the total value involved. Clearly, operational risk and credit risk don t always concentrate in the same segments.

How This Credit Risk Data Connects to PD Estimation

A GNPA ratio is a lagging, backward-looking measure. It tells you what has already defaulted, not what s likely to default next. That s the gap probability of default (PD) modelling closes. It s also why this Indian banking credit risk data is a genuinely useful input for PD work. It s not just a macro backdrop to quote in an introduction.

Two things from the data above feed directly into PD modelling in practice. First, the bank-category breakdown (PSB 2.50% vs PVB 1.73% vs Foreign 0.80%) is a natural segmentation variable. Pooling all SCBs into a single PD model would blur real structural differences in underwriting standards and portfolio.

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