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BlackLine, a provider of finance and accounting automation systems, employs me as the Chief Accounting Officer.
Burchell, Gary

A handful of financially savvy individuals recognized a troubling trend in a scene from the film The Big Short about the onset of the 2007-08 financial crisis: homeowners with excellent credit ratings were missing mortgage payments for the first time. The majority of Wall Street either didn’t notice or ignored it as a few small outliers in a huge mortgage industry. It turned out to be the forerunner of the world’s worst credit crisis in nearly a century.
When I was thinking about the introduction of the Financial Accounting Standards Board’s (FASB) credit loss standard, Current Expected Credit Losses (CECL), I remembered this scene. CECL’s deadline has been postponed three times, the latest recent in 2020 owing to the Covid-19 pandemic’s impact. The implementation date for banks and credit unions appears to be next year.
What does CECL have to do with The Big Short? The FASB intended for organizations to look beneath the iceberg’s tip when it issued the standard. A single missed mortgage payment could be a sign of future major credit problems. To avoid a repeat of the recent credit crisis, businesses must anticipate it by reporting losses on bad loans earlier than currently allowed by US generally accepted accounting principles (GAAP). As a result, debtholders would be able to recognize losses sooner and take mitigation measures sooner.
Non-bank entities with trade receivables and other balance-sheet credit exposures, such as manufacturers, retailers, consumer goods providers, and technology services providers like our company, have a bit of a reprieve for now, as the FASB has yet to set deadlines for other industry sectors with trade receivables and other balance-sheet credit exposures.
ADDITIONAL INFORMATION FOR YOU
In contrast to the existing requirement to posit known or incurred losses, banks and credit unions will soon be forced to estimate their expected credit losses and record them in their financial statements. My company, like all others, may need to do the same at some point. Forewarned is forearmed, as they say.
Looking Backwards And Forwards
While the CECL standard’s aim is obvious and sound, gathering and evaluating the necessary data to offer these insights is difficult. Instead of relying just on a review of previous payment patterns to accomplish compliance, a company should use some form of predictability or actuarial modeling tool to anticipate credit losses. Because of the inability to forecast future losses, CECL audit and compliance difficulties may arise.
Companies should prepare to gather and analyze both historical and real-time payment data, tracking customer payment behaviors to determine which ones pay on time, pay early, pay late, or don’t pay at all, to limit this risk and make the assumptions guiding the credit loss projection.
Customers that pay on time are obviously less at risk in terms of credit than those who are late on occasion or whose receivables suddenly lapse for several months after years of regular payments. Under the CECL norm, these consumers would give most people pause. As a result, credit estimating methods would need to investigate customer payment and non-payment data carefully to uncover anomalous patterns indicating potential credit losses, even for customers who pay “on time” but later than typical.
Because of the volume of data, these trends either don’t register on many firms’ radar screens or are difficult to spot without a systematic approach. A customer who is 30 days late for the first time, for example, may not raise any red flags. What makes you think that? In most cases, this would be seen as a single late payment for an otherwise healthy customer. However, under CECL, predictive analytics would ask the question of why an otherwise healthy consumer is only now missing their first payment.
It’s a fine line to walk. With one exception, the first technique looks at a payment pattern and judges it unduly optimistic; the new CECL approach needs financial professionals to assess why the trend changed quickly.
Because data is the lens through which credit defaults can be discovered, CFOs and controllers must guarantee that their finance and accounting (F&A) teams have real-time and reliable data. While the FASB still expects F&A teams to assemble and quote past payment trends, the prediction must include payment data up to and including the current month’s balance. If the team gets bogged down in manual processes and complicated spreadsheets, it will continually be playing catch-up, arriving too late to account for predicted credit losses.
Don’t forget to give credit where credit is due.
Here are some ideas to help you keep ahead of the game:
o When analyzing previous credit losses, look for “surprise” losses rather than those that were expected. What made these losses unique, and what could be done differently to prevent similar occurrences in the future?
o Now concentrate on the credit losses you “corrected.” Could you have foreseen or budgeted for these losses earlier? Were there any underlying trends in the data, such as macroeconomic, industry, or company-level trends, that could have served as a “canary in the coal mine”?
o Refine your processes based on the two stages above so that they can capture the additional data required to create a more educated credit loss assessment. Create a complete list of all data points and update it as necessary.
o Develop a streamlined and repeatable data evaluation procedure for systematically revising credit loss estimations and comparing them to actual experience.
· Consider automating the order-to-cash process with an AI technology that analyzes real-time accounts receivable data to estimate expected credit losses to decrease manual processing constraints.
Looking back at The Big Short, if F&A teams had tools to help them foresee credit losses at the time, they could have avoided or mitigated the global credit crisis that followed, albeit at the expense of a captivating film.
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