How can commercial banks better assess credit risk to reduce uncertainty in a downturn?

The last time there was a deterioration in the credit cycle was 14 years ago, during the ‘08 financial crisis, so it’s been an exceptionally long bull market considering the average economic cycle in the US has lasted roughly 5.5 years since 1950.

Over the last decade, we’ve seen historically cheap credit, and despite numerous challenges, including a global pandemic, a tightening labor market, and a war in Europe, economies in the West have continued to grow. However, it’s now clear the tide is changing - IMF managing director, Kristalina Georgieva, recently warned that a third of the global economy will be hit by recession this year. Even if the US manages to avoid this, the expectation is that there will still be a significant slowdown in its economic growth for the next 18-24 months. 

As an industry, how prepared are commercial banks for the turn in the credit cycle? How can we satisfy our boards, our regulators, and ourselves that we have a strong forward-looking view of risk and that we won’t stop lending to customers once the economic situation gets tougher?

The ‘08 financial crisis accelerated the use of stress testing by regulators with the largest banks needing to conduct supervisory stress tests on an annual basis. This stress testing, when carried out at an individual borrower level, takes a fundamental approach – i.e. a credit analyst constructs a financial model (usually using Microsoft Excel) to simulate the cash flow, balance sheet, and income statement of the business. They then project this forward for the lifetime of the loan and use assumptions to ‘sensitize’ or stress this model to observe the performance of the business under adverse circumstances. This modeling is “augmented” by peer group analysis at a macro and sector level (typically looking at a dozen or so sectors), where a prospective borrower is compared with other similar businesses in order to establish reasonable expectations for future performance. 
 
The issues with this approach are two-fold: firstly, it assumes that tomorrow will be a lot like yesterday which is unhelpful given every recession is different. And secondly, most businesses are more or less alike, which misses their unique differences. In a recessionary scenario where consumer spending is tightened, the experience of a budget downtown hotel for example will likely be very different from a luxury resort. The same can be said for food & drink, retail businesses, etc. 
  
Moving away from an Excel-based to a more data-led and automated approach gives lenders the opportunity to build models that are far more specific to a given business. This is because they are accurately modeling the conditions of the business plan or capturing the nuances of a granular industry. This allows lenders to take a much more granular and rigorous approach to building stress scenarios, using the data to identify clusters of sectors that respond to similar macroeconomic factors, and then modeling the effects of shocks to these factors as the basis of the scenario. The foresight gained from this approach can help identify potential problems much sooner, enabling lenders to be smarter and faster in their decisions about which loans to do and how to structure them.