New credit loss norm could hurt

UPSC Syllabus: Gs Paper 3- Indian economy

Introduction

The Reserve Bank of India is shifting banks from the Incurred Loss (IL) model to the Expected Credit Loss (ECL) framework from April 1, 2027. This moves lending from a backward-looking approach to a forward-looking system. Banks must now anticipate future defaults and make provisions early. While this strengthens financial stability, it raises a concern that stricter provisioning may reduce credit access, especially for borrowers without strong collateral.

What is ECL Framework?

1. Meaning of ECL: Expected Credit Loss (ECL) is a method where banks estimate possible future loan losses in advance. It focuses on what may happen, not only on what has already happened.
2. Shift from IL to ECL: Under the earlier system, banks recognised losses only after default. Under ECL, banks must provide for losses even when loans are still performing.
3. Shift from past loss to future risk: ECL moves the focus from “what has happened” to “what may happen”. Banks must assess future vulnerability using expected economic conditions, not just past repayment behaviour.
4. Three-stage framework: Loans are classified into Stage 1, Stage 2, and Stage 3 based on increase in credit risk. Stage 1 requires 12-month expected loss, while Stage 2 and Stage 3 require lifetime loss coverage.
5. Key risk variables: ECL uses Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) to measure future credit risk in a structured way.

Key Changes in Credit Risk Assessment

1. Beyond 90-day NPA norm: The 90-day overdue rule remains, but risk identification can happen earlier through forward-looking assessment.
2. Early warning signals: Risk is now identified using signals like 30+ days delay in payment, rating downgrades, financial stress of the borrower, or weakness in a particular sector. These indicators help banks detect future problems before they become defaults.
3. Stage movement flexibility: Loans can move from Stage 1 to Stage 2 even without default if risk increases. This shift forces banks to make higher provisions earlier, which increases the cost of lending.

Impact on Banking Behaviour

1. Higher provisioning pressure: ECL requires banks to recognise losses early, increasing capital burden, especially for risky or unsecured loans.
2. Dependence on collateral: Around 75% of ₹15,905,063 crore advances are secured, but collateral value can fall sharply during crises, reducing recovery rates.
3. Fire-sale effect on collateral value: During systemic stress, collateral assets are often sold quickly at lower prices. This reduces recovery value, increases Loss Given Default (LGD), and raises provisioning pressure for banks.
4. Rising unsecured loans risk: Unsecured advances increased from 15.7% in 2013 to 25.3% in 2025, raising Loss Given Default (LGD) and forcing banks to be more cautious.
5. Shift towards safer lending: Banks may prefer collateral-backed loans and avoid unsecured lending due to high provisioning requirements.

Credit Rationing & Economic Implications

1. Interest rate fails as a risk tool: As explained by economists Joseph Stiglitz and Andrew Weiss, increasing interest rates does not reduce risk effectively. When rates rise too much, safe borrowers leave because their profits decline, while risky borrowers remain since they are willing to take higher chances.
2. Adverse selection problem: Economist George Akerlof explains that this process creates a “lemon” pool, where mostly risky borrowers remain in the market. This lowers the overall quality of borrowers and increases default risk for banks.
3. Credit rationing outcome: To avoid this situation, banks do not keep raising interest rates. Instead, they limit lending and give loans only to selected borrowers, especially those who can provide strong collateral.
4. Self-selection among borrowers: When banks demand collateral, safe borrowers agree because they are confident of repayment. Risky borrowers avoid pledging assets, as they expect a higher chance of default, which sharpens borrower sorting under ECL.
5. Higher PD and provisioning burden: Loans given to risky borrowers lead to higher Probability of Default (PD). Under ECL, this increases provisioning requirements, and the cost may become higher than the expected return, discouraging banks from lending further.
6. Credit access vs asset ownership dilemma: The system may favour borrowers with strong collateral over those with real repayment capacity. This creates a situation where asset ownership becomes more important than actual creditworthiness.

Forward-Looking Risks in ECL System

1. Macroeconomic sensitivity of PD: Banks must include economic conditions in PD models, making loan classification sensitive to shocks.
2. Stage transition risk: Even small stress can push loans from Stage 1 to Stage 2, requiring lifetime provisioning and increasing cost.
3. Sectoral credit tightening: Banks may stop lending to sectors showing early signs of weakness to avoid higher provisioning.
4. Missing dimension (flow gap): There is limited discussion on how smaller banks will manage data and modelling challenges under ECL.

Conclusion

ECL improves financial stability by enabling early risk detection and better loss recognition. However, it increases provisioning pressure and shifts lending towards collateral-backed loans. This may exclude capable but asset-light borrowers and lead to credit rationing. Its success depends on effective data use, balanced implementation, and ensuring that credit access is not sacrificed for prudence.

Question for practice:

Discuss how the shift from the Incurred Loss model to the Expected Credit Loss framework may improve financial stability while also leading to credit rationing in India.

Source: Businessline