How Commercial Banks Can Better Assess Credit Risk To Reduce Uncertainty In A Downturn
When it comes to commercial lending, a robust strategy to credit risk management is essential, and it becomes even more crucial in times of uncertainty and downturn. Accurately and efficiently determining the creditworthiness of new or returning borrowers will help both traditional and newer lenders in making faster and smarter decisions about lending to more businesses and achieving better outcomes.
Given the current unprecedented economic and geopolitical problems, traditional credit scoring models, using historical data, will have limitations in predicting credit risk. Consequently, banks would need to adopt a data-driven and automated approach to create models that are significantly more tailored to a particular business. Commercial lenders can use several methods to better assess credit risk and reduce uncertainty during economic downturns, some of which are outlined below:
Strengthening credit risk management processes and conducting regular stress tests
Banks should implement robust credit risk management processes such as maintaining an appropriate credit administration and monitoring process and sound procedures related to asset quality assessment, etc. to control risks in the event of any macroeconomic shock. Additionally, banks need to undertake a granular and rigorous approach to building stress scenarios that accurately model the conditions of the business and capture the intricacies of an industry.Stress testing involves simulating the effects of various economic scenarios, such as a recession or a decline in asset prices, on a bank's portfolio. This can help banks to identify potential areas of weakness in their loan portfolios and take pre-emptive measures to mitigate credit risk.
Proactive monitoring of loan book
The economic environment in the last few years with numerous shocks and events such as commodity price inflation, supply chain issues, and fundamental changes due to COVID-19 have proven that banks need to be more proactive in monitoring their portfolio. Banks can anticipate borrower financial troubles, industry-related risk, and covenant breaches well in advance by maintaining a proactive and ongoing underwriting view of their entire loan book rather than just at origination and yearly review. This helps spot potential issues much earlier, resulting in lower losses and better customer relationships by managing ahead of financial troubles and potential default events.
Employing data driven approach and forward looking insight
The COVID-19 pandemic serves as evidence that the conventional approach to commercial lending which relies on historical data, financial modelling of a base case, worst case, and best case scenario, and yearly reviews is unsuitable for dealing with adverse events. These versions work well during uneventful times. The standard models, however, proved unservice able for unprecedented events such as the pandemic because historical correlations were disrupted.
”Banks can anticipate borrower financial troubles, industry related risk, and covenant breaches well in advance by maintaining a proactive and ongoing underwriting view of their entire loan book rather than just at origination and yearly review”
Instead of depending solely on a company's past performance, it's critical for lenders to use data insights to create a clear picture of a borrower’s future growth potential. Lenders need to take a granular,forward looking view of their lending portfolio by combining external data sets, including macroeconomic indicators, sector specific drivers and non traditional data sources. Banks must use multiple data sources, including alternative data that often has less lag than traditional sources, and leverage AI/ML and analytics to identify new risk groups and predict the probability of loan default.
At OakNorth, we apply data analysis techniques to create unique models that provide a forward looking view at industry level. By combining borrower provided data with our vast repository of external data, we are able to add depth to point in time analysis and monitoring, allowing lenders to be smarter and faster in their decisions on loan approval and driving better outcomes during economic cycles.
Given the current unprecedented economic and geopolitical problems, traditional credit scoring models, using historical data, will have limitations in predicting credit risk. Consequently, banks would need to adopt a data-driven and automated approach to create models that are significantly more tailored to a particular business. Commercial lenders can use several methods to better assess credit risk and reduce uncertainty during economic downturns, some of which are outlined below:
Strengthening credit risk management processes and conducting regular stress tests
Banks should implement robust credit risk management processes such as maintaining an appropriate credit administration and monitoring process and sound procedures related to asset quality assessment, etc. to control risks in the event of any macroeconomic shock. Additionally, banks need to undertake a granular and rigorous approach to building stress scenarios that accurately model the conditions of the business and capture the intricacies of an industry.Stress testing involves simulating the effects of various economic scenarios, such as a recession or a decline in asset prices, on a bank's portfolio. This can help banks to identify potential areas of weakness in their loan portfolios and take pre-emptive measures to mitigate credit risk.
Commercial lenders can use several methods to better assess credit risk and reduce uncertainty during economic downturns
Proactive monitoring of loan book
The economic environment in the last few years with numerous shocks and events such as commodity price inflation, supply chain issues, and fundamental changes due to COVID-19 have proven that banks need to be more proactive in monitoring their portfolio. Banks can anticipate borrower financial troubles, industry-related risk, and covenant breaches well in advance by maintaining a proactive and ongoing underwriting view of their entire loan book rather than just at origination and yearly review. This helps spot potential issues much earlier, resulting in lower losses and better customer relationships by managing ahead of financial troubles and potential default events.
Employing data driven approach and forward looking insight
The COVID-19 pandemic serves as evidence that the conventional approach to commercial lending which relies on historical data, financial modelling of a base case, worst case, and best case scenario, and yearly reviews is unsuitable for dealing with adverse events. These versions work well during uneventful times. The standard models, however, proved unservice able for unprecedented events such as the pandemic because historical correlations were disrupted.
”Banks can anticipate borrower financial troubles, industry related risk, and covenant breaches well in advance by maintaining a proactive and ongoing underwriting view of their entire loan book rather than just at origination and yearly review”
Instead of depending solely on a company's past performance, it's critical for lenders to use data insights to create a clear picture of a borrower’s future growth potential. Lenders need to take a granular,forward looking view of their lending portfolio by combining external data sets, including macroeconomic indicators, sector specific drivers and non traditional data sources. Banks must use multiple data sources, including alternative data that often has less lag than traditional sources, and leverage AI/ML and analytics to identify new risk groups and predict the probability of loan default.
At OakNorth, we apply data analysis techniques to create unique models that provide a forward looking view at industry level. By combining borrower provided data with our vast repository of external data, we are able to add depth to point in time analysis and monitoring, allowing lenders to be smarter and faster in their decisions on loan approval and driving better outcomes during economic cycles.
