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CECL Is Coming Is Your Credit Union Prepared?

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BY CORINNE KALSKY

The way your credit union calculates its Allowance for Loan and Lease Losses is about to be shaken up. Are you ready for the impact the new FASB Accounting Standards Update will have on your CU? This article will poise you to handle the current expected credit losses methodology that will stem from that update.

It’s been a long time coming.

Following years of research, proposals and industry commentary dating back to 2008, the Financial Accounting Standards Board (FASB) has released its final Accounting Standards Update (ASU), which addresses the accounting for credit losses. Released on June 16, 2016, this new standard introduces the current expected credit losses (CECL) methodology and will significantly impact how credit unions and other financial institutions calculate their Allowance for Loan and Lease Losses (ALLL) in the future.

Yet there is no need for credit unions to panic. Non-SEC filers, including credit unions, have until the fiscal year beginning after December 15, 2020 to implement the new standard. Public business entities that file with the SEC must implement in the fiscal year beginning after December 15, 2019. All institutions may select early adoption starting with fiscal years that begin after December 15, 2018.

2020 may seem far off, but credit unions should start preparing now for implementation. The new rule will require banking institutions to collect different and more robust loan portfolio data than they have in the past. It will also mandate that they adjust the methods they employ to determine expected credit losses, a key factor in the loan loss reserve calculation.

Why the Change?

Most experts agree that the old incurred loss model for predicting credit losses is flawed. It is based on a determination of “probable” losses that relies largely on past history and outdated information. The recent financial crisis brought the inadequacies of this methodology starkly to light, as past history offered highly inaccurate predictive capabilities during the deteriorating economic conditions of the dramatic real estate market collapse of the mid-2000s. At many institutions, this scenario resulted in credit losses that quickly devoured loan reserves, and then some.

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