Moving away from a traditional credit focused approach

With the pandemic behind us, the moratorium and restructuring opportunities announced by the RBI to help borrowers faced with financial difficulties to bridge the cash flow disruption caused by lockdowns and general business dislocation, are coming to an end. However, as the moratorium measures as well as the one-time restructuring opportunities come to an end, banks may start seeing a substantial rise in the bad loans. As per the RBI Financial Stability Report released in July 2021, macro stress tests indicate that the Gross Non-Performing Asset (GNPA) ratio of SCBs may increase from 7.48% in March 2021 to 9.80% by March 2022 under the baseline scenario; and to 11.22% under a severe stress scenario.

The current economic climate continues to remain uncertain, and financial institutions need to be able to identify bad credit from good credit early on. Financial institutions today focus on using multiple generic criteria such as macro-economic indicators, overall industry trends, and internal operational parameters such as compliance with regulatory filings, point-in-time stock audits, related-party transactions, promoter holding pattern and high value payments trends for the corporate and SME portfolio. On the retail side, early warning indicators are focused on overall portfolio analytics and limited statistical models that can run on the back of credit bureau data and borrower payment behavior. These current set of tools and processes that are used by the financial institutions provide only vague answers on the impending stress in the loan book and are not able to investigate the extent of the problem. For example, using macro-economic trends or overall industry sentiments only establish trends but don’t provide any insight into the actual credit problem at a borrower level. Similarly, using standard credit risk models and financial ratios provide outdated analysis and ignores the obvious fallacies in free cash flow projections. Due to the inherent nature of these signals being indicative, financial institutions are also limited in their ability to define remediation actions to prevent further deterioration in their loan book.

While financial institutions understand the need for early warning signals (EWS) to distinguish bad credit from good credit, re-running old models and old indicators only leads to vague plausible answers to identify credit stress. EWS tools need to scale up beyond generics and focus on identifying specific issues with the borrowers to distinguish bad credit from credit needing liquidity support to tide through. The scope of the EWS mechanism also needs to increase beyond identifying credit-related stress. EWS measures need to become broad-based to include factors such as AML, fraud, market and liquidity risk measures to be able to identify money laundering, funds diversion, fraud and other liquidity and solvency concerns.

With both the scope and approach of EWS requiring a massive change, financial institutions need a complete overhaul of technology and thought process to surmount this challenge. From a reactive checklist-based approach focused on limited macro-economic and internal organizational factors, financial institutions need to focus on leveraging data and machine learning models to gain borrower specific insights across multiple dimensions such as credit, liquidity, fraud and AML. Using techniques and technologies such as web scraping, APIs and open data frameworks, the universe of data sources from bureau data to alternative data sources, social media feeds, news events and macro-economic data have become easily available. These need to be combined with the borrower-specific information that is available with the financial institution to generate on-demand and specific insights on borrower behavior and risk predictions.

Irrespective of the size and portfolio of the financial institution, taking a comprehensive view of borrower specific EWS is the need of the hour. The EWS framework provided by the regulator is a starting point but EWS systems to predict operational, environmental and / or financial stress in borrower accounts early on using the power of data will become extremely critical for banks and financial institutions Financial institutions should look at investing in intelligent EWS platforms which have integrated data pipelining mechanisms to ingest data from multiple sources and a flexible interface to cover both qualitative and quantitative methods of identifiers across demographic, behavioral, operational, financial, managerial, and environmental factors. Using metrics like the EWI hit ratio, asset group performance and remediation / response action performance, the built-in models of an EWS can go through a constant calibration process to improve prediction accuracy.

With this, EWS systems using a traditionally reactive or point-in-time method, can be revamped to be able to make them relevant to the current situation.



Views expressed above are the author’s own.



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