At the height of the pandemic, India’s fiscal and monetary authorities announced a series of measures to help those affected. The most important of these measures were moratoriums and restructuring opportunities to help address cash flow disruptions caused by bottlenecks and general business dislocation.
The moratoriums and restructuring gave leeway to all borrowers, including those who were stressed before and regardless of the crisis. As the moratorium measures liquidation, there is likely to be a significant increase in bad debts.
According to the RBI’s Financial Stability Report released in July 2021, macro stress tests indicate that the gross non-performing assets (GNPA) ratio of SCBs could drop from 7.48% in March 2021 to 9.80% by March 2022 in the baseline scenario; and 11.22 percent in a severe stress scenario.
With the current economic climate remaining uncertain, financial institutions must be able to distinguish bad credit from loans requiring liquidity support to get by. Financial institutions must also be able to quickly identify fraud and embezzlement. The profitability of financial institutions and, in general, the stability of the financial system, depends on the early identification and rapid elimination of warning signs from the credit portfolio. This job to do requires that they have the right set of tools and processes to be able to separate bad debts from borrowers in need of liquidity support. The key is not only to be aware that there is impending stress in the loan portfolio, but also to be able to look through the depth of the problem at hand.
Financial institutions need to upgrade or invest in early warning signal tools (EWS) to overcome this challenge. From reactive credit risk metrics to models that examine cash flow and predict losses, from financial ratios to machine learning models that identify fraud and embezzlement, EWS tools must become mature. Separating the wheat from the chaff is a challenge forcing financial institutions to upgrade their technology and thinking process to EWS.
Divide and divide your borrowers into several asset classes to assess the impact of credit factors
Traditionally, borrowers have been classified into asset groups solely on the basis of their product characteristics, industry, or demographics. However, recent times have shown us that borrowers who may not have similarities in these seemingly obvious characteristics can still behave consistently. Be prepared to monitor the portfolio by multiple asset classes for information and warnings about borrower behavior.
Leverage the power of data and don’t limit it to internal or organizational data sources only
With the advent of web scraping techniques, APIs and open data frameworks, the universe of data sources, from desktop data to alternative data sources, social media feeds, news events and news macroeconomic data, has become easily accessible. While these have always been around, the ease of use to ingest these datasets into SAP tools, combined with borrower-specific data, helps generate on-demand, trigger-based and predictive insights. which were largely difficult to obtain.
Invest in constant learning, unlearning and relearning for credit factors and associated EWIs
Using the regulatory framework on SAP as a basis, it is necessary to build a rich repository of credit factors and early warning signals) which covers both qualitative and quantitative methods of demographic, behavioral, operational identifiers, financial, managerial and environmental. The factors. Using metrics such as EWI success rate, asset pool performance, and remediation / response action performance, the built-in models of an EWS can go through a constant calibration process to improve the accuracy of the results. predictions.
Back-test credit and rating risk models with SAP history and results
Traditionally, credit risk and rating models have been the gatekeepers of credit flows. The gatekeepers have seldom done their job, as the theory of financial ratios has gone beyond the obvious mistakes of cash flow projections. EWS offers the ability to challenge goalies and adjust their settings to prevent bad credits from being left behind.
Don’t get distracted by macros
Macroeconomic indicators can be good for setting and reinforcing trends, but each credit turns good or bad on its own merit. The traditional approach of using macros to indicate stress and pumping up big data to strengthen a hypothesis only increases bias errors. SAP must be able to predict likely outcomes based on the business model and specific cash flow patterns of each borrower and institution.
SAP tools have never been more important for financial institutions. Yet financial institutions risk re-running old models and indicators to get vague and plausible answers to identify credit stress instead of looking for specific answers. SAP tools must evolve beyond generics. Otherwise, real moratoriums and restructuring will be bludgeoned by bad credit and thwart the whole objective of regulatory and fiscal abstention.
The author, Viraj Shah, is a leader at Business Solution, Acies. Opinions expressed are personal
First publication: STI