By Josh Stein
WWhether institutions use the CECL methodology or the losses incurred, estimating credit losses in the current pandemic economic environment is a challenge, to say the least.
With government-approved shutdowns and resulting job losses countered by extraordinary loan-forbearance requirements and congressional stimulus measures, bankers are forced to play the role of epidemiologist, Keynesian forecaster, and of fortune teller. Those who turn to traditional credit measures, such as delinquent loan levels and distressed debt restructurings, for example, have been frustrated this first quarter, as the suddenness of lockdowns and the size of government programs have essentially mitigated these trends. Bankers know, however, that credit losses exist somewhere and could be significant.
Based on CECL’s forecasts, provisions for energy credit losses have increased, with coverage ratios of up to 20%. With the exception of targeted provisions for hospitality and retail, however, the remaining provisions for commercial loan losses in the first quarter were relatively subdued, as expected guarantee prices remain firm: albeit increasing substantially. compared to 2019 levels, they are generally between 100 and 200 basis points.
In credit card loans, coverage ratios approached 10-12.7% for some portfolios. Growing provisions in residential mortgage portfolios have been similar to those in commercial loans: although rising, coverage ratios remain rather low (less than 100 basis points) while house prices remain strong. That said, it is significant that the CECL forecast was made in a rapidly deteriorating economy – most banks assumed unemployment rates of around 10% – well below unemployment levels of 14.7%. % announced by the Ministry of Labor in April.
With unemployment reaching 10% during the 2007-2010 recession, it is difficult to see how April’s unemployment rate of 14.7% could fit well into current models for estimating credit losses. And given Federal Reserve Chairman Jerome Powell’s comment that the unemployment rate could reach 20-25% in May or June, estimating losses then often becomes a “Q-factor” exercise, as Qualitative adjustments of the models seem to determine the day. In fact, many state-owned banks reporting their first quarter results have focused on important qualitative adjustments from the results derived from the model.
In light of this, the importance of qualitative adjustments has never been higher, especially as newly adopted auditing standards focus on assessing “management bias” in these estimates. In response to the new auditing standard, the AICPA’s practical help on credit losses refers to auditing various aspects of management bias – availability, anchoring, confirmation, and familiarity bias – all of which require structured governance responses and internal controls to make sense of the reasonableness of Q factor adjustments that result.
Josh Stein is vice president of accounting and financial management at ABA.