Expected Loss-based Approach for Bad Loan Provisioning

The Reserve Bank of India (RBI) has outlined its plans for the upcoming financial year, 2023-24. Among the proposed measures to strengthen the bad loan resolution ecosystem is the introduction of an expected loss-based approach for provisioning. This method allows banks the autonomy to create their own models for estimating credit losses and distribute increased provisions over a span of five years.

Expected Loss-Based Approach

The RBI aims to enhance the efficiency and flexibility of provisioning by implementing an expected loss-based approach. This approach allows banks to customize their credit loss models based on their individual portfolios. By doing so, banks can more accurately assess and manage potential credit losses. Additionally, the higher provisions can be spread out over a five-year period, easing the financial burden on banks.

Policy Measures in the Pipeline

Alongside the expected loss-based approach, the RBI is expected to announce other policy measures during the fiscal year 2023-24. Guidelines on the securitization of stressed assets, as well as a comprehensive review of the prudential framework, are on the agenda. These measures aim to further strengthen the resolution ecosystem and promote effective risk management in the financial sector.

Discussion Paper Release

The RBI had previously released a discussion paper on the expected loss-based approach for provisioning in January of the current year. This paper laid the groundwork for the proposed approach and facilitated industry-wide consultations and feedback. The input received during this process helped shape the final guidelines to ensure their relevance and effectiveness.

Classification of Financial Assets

Under the expected loss-based approach, financial assets are classified into three categories: Stage 1, Stage 2, and Stage 3. These classifications are based on the assessed credit losses associated with each asset. Banks are required to classify their financial assets at the time of initial recognition and on subsequent reporting dates. Necessary provisions must be made based on these classifications to ensure prudent risk management.

Mitigant Concerns and Model Risk

The RBI’s discussion paper highlighted mitigant concerns relating to model risk and significant variability. While banks are given the flexibility to design their own credit loss models, it is essential to address potential risks associated with the accuracy and reliability of these models. The RBI recognizes the need for proper safeguards and risk management practices to mitigate any potential adverse impacts.

Enhancing Financial Stability

Recent financial sector turmoil in the US and Europe has prompted a reassessment of risks to the financial stability and resilience of financial institutions. In response, the RBI aims to stress test Indian banks and non-banking financial intermediaries to ensure their soundness and resilience. Constant review and strengthening of capital buffers and liquidity positions are vital to safeguard the financial system.

Advanced Supervisory Analytics Group (ASAG)

To enhance supervisory inputs and oversight, the RBI has established the Advanced Supervisory Analytics Group (ASAG). This group focuses on leveraging technology to develop advanced analytics capabilities. They are developing use cases such as social media analytics, KYC compliances, and governance effectiveness using machine learning models. These tools enhance supervisory effectiveness and improve regulatory oversight.



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