RBI’s ECL Norms: How Some Banks Are at the Forefront of Using Analytical Tools

The Chief Information Officer (CIO), Chief Risk Officer (CRO) and the Chief Financial Officer (CFO) will have to work in tandem to implement the enhanced Expected Credit Loss (ECL) norms recently announced by the Reserve Bank of India (RBI), as per experts. The revised ECL norms aim at bringing about a shift in how banks assess and provision for credit risk. Moving away from the traditional ‘incurred loss’ model, where losses were computed only after they realised, the ECL regime requires banks to project potential loan defaults upfront using data-driven models and forward-looking analytics. 

The overarching goal is to enhance the accuracy, timeliness, and resilience of the banking system’s risk management, aligning Indian practices with global Basel standards while driving a deeper integration of technology, data governance, and predictive intelligence in banking operations. The norms come into effect  from April 1, 2027.   

Credit Recovery and IT Should Work Hand in Hand

Banks will have to create a credit data warehouse, so that the anticipated credit loss can be worked out, and can be estimated using the best analytical tools. It will provide various pointers to understand the credit loss. Sekar S, Advisor to the Board and former General Manager & CIO, Karur Vysya Bank (KVB) says, “For example, if it is a public limited company, if there is frequent attrition at the board of directors level, then it’s a red flag. Such red flags are already monitored by the banks, which may not be on the financial side but more on the operational level, transactional level points. They are all monitored already by various banks at various levels. But the higher maturity level on this, is to ascertain the anticipated credit loss, which requires quality of data and an understanding of the customer repayment cycle. What is the repayment history, all that will have to be very clearly documented to ascertain an anticipated credit loss. That is feasible and necessary technologies are already available in the market and it can be implemented easily.”

However, to ascertain success, the credit recovery department and IT should work together, feels Sekar, “Primarily, what is an IT strategy ? IT strategy for any organization is aligning with business. Credit recovery is also a business. Credit recovery is not only a collection business. It is part of business because NPA is a very important parameter which can affect the profitability of the bank. Thus, the CIO will have to work in close coordination with the credit department.”

The credit recovery department in each bank concentrates on this anticipated loss by using various analytical tools. Different banks are at various levels in using these tools. “The maturity level at various banks is differing now. Some banks are in a very good advanced position on this. How ? One is the monitoring at the Special Mention Account (SMA) levels, SMA0, SMA1, SMA2 level itself. To do that SMA level monitoring, technology is highly required. The accounts are monitored closely and then the recovery process is worked out,” informs Sekar.

Modernising Data and Risk Infrastructure

To comply with RBI’s Expected Credit Loss (ECL) framework, Bank CIOs and CTOs must modernize their data and risk infrastructure to support forward-looking provisioning. “This involves automating credit risk workflows, integrating Regulatory Probability of Default (PD)/Regulatory Loss Given Default (LGD)/Exposure at Default (EAD) models into core systems, and ensuring robust data lineage across the lifecycle of credit exposures. They must also build scalable platforms for model development, deployment, and monitoring, with strong governance and auditability,” says Biswajeet Mahapatra, Principal Analyst, Forrester.

CFOs are expected to assess the capital impact of ECL provisioning, align financial reporting with staging and risk migration, and ensure transparent disclosures. CROs play a critical role in defining staging criteria, overseeing model risk governance, and validating assumptions through independent testing and benchmarking.

“Together, these roles must collaborate to ensure regulatory compliance, operational readiness, and strategic alignment with RBI’s glide path for ECL adoption,” says Mahapatra. 

Challenges

The transition from the incurred loss model to the Expected Credit Loss (ECL) framework introduces several technology and data management challenges for banks. One major hurdle according to Mahapatra, is consolidating data from different systems, “ The challenge will be the integration of granular, forward-looking data from multiple systems, which often suffer from inconsistencies, missing values, and limited traceability.”

Another challenge lies in the complexity of ECL models, “Which require dynamic inputs like probability of default, loss given default, and exposure at default, demanding robust infrastructure for model development, validation, and governance,” says Mahapatra. 

Handling legacy systems is another major hurdle, “Legacy systems are typically not equipped for real-time risk assessment or predictive analytics, making it difficult to automate staging decisions and monitor credit deterioration continuously. Digital tools can help address these issues by using AI and machine learning to improve data quality and predictive accuracy, leveraging advanced analytics for scenario modeling and stress testing, and deploying cloud platforms to provide scalable, secure environments for storing, processing, and validating large volumes of structured and unstructured data efficiently,” suggests Mahapatra.