The way forward for banks in implementing the ECL model

The Reserve Bank of India (RBI) recently released a discussion paper on the transition of banks from the incurred-loss approach to the expected credit loss (ECL) model. Under the existing guidelines, banks make loan-loss provisions based on the "incurred loss" approach, but the ECL model considers the anticipation of default over the lifetime of an asset. The central bank has proposed that banks will be allowed to design their own credit loss models and spread the impact on capital over a five-year period or less.

The adoption of the ECL model will require banks to classify loans into three stages based on the overdue position of the asset. Banks will have to compute probability of default (PD), loss given default (LGD), and exposure at default (EAD) at the loan level, based on the historical data of borrowers in its different portfolios. The aim is to enable institutions to take early action to mitigate potential credit losses, which can reduce the impact of credit risk on their financial performance. Board and senior management will be responsible for credit risk practices and the adequacy of ECL frameworks.

However, the implementation of the ECL framework poses some key challenges for banks:

• Sufficient volume of historical default data required to build robust PD models - banks may face difficulties if segment-wise risk profiles are not maintained effectively.
• Robust and flexible systems required to undertake voluminous complicated calculations within short time-period and using a large amount of data collected from multiple sources.
• Pricing models for banks will need to be aligned with the expected loss models to account for such costs at the acquisition stage.
• Availability of resources and skills crucial to understand, build, and manage ECL models.
• Implementation bottlenecks faced in building the right model, complying to regulatory requirements, streamlining of systems and processes, training of employees, etc.
• Implementation of ECL could mean higher provisioning costs for banks that may affect their profitability in the short-term.
• Changes in assumptions and parameters of the model may change ECL results drastically, leading to high volatility in financial results that may impact growth plans and risk mitigation plans for banks.

Despite the challenges, the introduction of ECL guidelines is a positive step in the right direction, and banks must build systems to adopt a more proactive approach to credit risk management. Some of the guidelines suggested by RBI in the discussion paper may evolve based on feedback received from banks/others. Adoption of these guidelines would lead to banks adopting more data-driven analytical models for credit risk management. The ECL migration proposed by RBI is a positive step towards better credit risk management and banks must take proactive measures to ensure a successful implementation and compliance with regulatory requirements.

ICRA Analytics can help banks comply with the new ECL norms by providing ECL modelling and IT solutions that help build accurate and robust expected credit loss models. With an experience of over 5+ years in ECL modelling, we can assist banks in complying with regulatory requirements and developing portfolio specific models. Our in-house developed solution, iECL, can integrate with multiple LOS and Core Banking systems to undertake voluminous complicated calculations within a short time and make the ECL computation process much easier and error-free.

By partnering with ICRA Analytics, banks can overcome the challenges posed by the ECL framework and ensure successful implementation and compliance with regulatory requirements. Don't let the transition to the ECL model hold you back, let us help you adopt a more data-driven analytical approach to credit risk management.

Know more about iECL, here!