Move away from a traditional credit-focused approach
With the pandemic behind us, the moratorium and restructuring opportunities announced by the RBI to help borrowers facing financial difficulties overcome the cash flow disruption caused by lockdowns and general business disruption, are coming to an end. However, as moratorium measures as well as one-off restructuring opportunities come to an end, banks could start to see a substantial increase in bad debts. According to the RBI Financial Stability Report released in July 2021, macro stress tests indicate that the gross non-performing assets (GNPA) ratio of SCBs could rise from 7.48% in March 2021 to 9.80% by March 2022 in the baseline scenario; and 11.22% in a severe stress scenario.
The current economic climate continues to be uncertain and financial institutions need to be able to distinguish bad credit from good early on. Financial institutions today focus on the use of several generic criteria such as macroeconomic indicators, overall industry trends and internal operational parameters such as compliance with regulatory filings, inventory audits at a given time , related party transactions, sponsor ownership pattern and high value. payment trends for corporate and SME wallet. On the retail side, early warning indicators focus on overall portfolio analysis and limited statistical models that can rely on credit bureau data and borrower payment behavior. This current set of tools and processes used by financial institutions provides only vague answers about impending stress in the loan portfolio and does not allow investigation of the extent of the problem. For example, the use of macro trends or general industry sentiment only establishes trends, but does not provide insight into the actual credit problem at the borrower level. Similarly, using standard credit risk models and financial ratios provides outdated analysis and ignores obvious errors in free cash flow projections. Due to the intrinsic nature of these signals being indicative, financial institutions are also limited in their ability to define corrective actions to prevent further deterioration of their loan portfolio.
While financial institutions understand the need for early warning signals (EWS) to tell bad credit from good credit, re-running old models and indicators only leads to vague plausible answers to identify credit stress. SAP tools should expand beyond generics and focus on identifying specific issues with borrowers to distinguish bad credit from credit requiring liquidity support to get through. The scope of the SAP mechanism must also extend beyond the identification of credit-related stress. SAP measures should be generalized to include factors such as anti-money laundering, fraud, market and liquidity risk measures in order to be able to identify money laundering, embezzlement, fraud and other liquidity and solvency issues.
With the scope and approach of SAP requiring massive change, financial institutions need a complete overhaul of technology and thought process to overcome this challenge. From a reactive checklist-based approach focused on limited internal organizational and macroeconomic factors, financial institutions should focus on leveraging data and machine learning models to gain insights specific to the business. borrower in multiple dimensions such as credit, liquidity, fraud and anti-money laundering. Using techniques and technologies such as web scraping, APIs and open data frameworks, the universe of data sources ranging from desktop data to alternative data sources, social media feeds, events news and macroeconomic data has become readily available. These should be combined with borrower-specific information available from the financial institution to generate specific, on-demand information on borrower behavior and risk predictions.
Regardless of the size and portfolio of the financial institution, having a holistic view of SAP specific to the borrower is the need of the hour. The SAP framework provided by the regulator is a starting point, but SAP systems to predict operational, environmental and/or financial stresses in borrower accounts early on using the power of data will become extremely critical for banks and creditors. financial institutions. Financial institutions should consider investing in smart EWS platforms that have integrated data pipeline mechanisms to ingest data from multiple sources and a flexible interface to cover qualitative and quantitative methods of identifiers across demographics, behavioural, operational, financial, managerial and environmental. Using metrics such as EWI hit rate, asset group performance, and remediation/response performance, the built-in models of an EWS can go through a constant calibration process to improve accuracy. forecasts.
With this, EWS systems using a traditionally reactive or ad hoc method can be revamped to be able to make them relevant to the current situation.
The opinions expressed above are those of the author.
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