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More data required for Expected Loss Model

February 21, 2014
Read Time: 0 min

It has been well documented that the FASB’s Current Expected Credit Loss (CECL) model, as it is presently proposed, will require banks of all sizes to recognize an immediate allowance for credit losses that represents all future expected losses to be incurred, as opposed to only those losses that are probable over the next 12 months as dictated by the current Incurred Loss model.  Industry experts, including the OCC’s Thomas Curry, have predicted this change will likely result in an increase to a bank’s Allowance for Loan and Lease Losses (ALLL) reserve of between 30-50 percent

As many institutions await the approval and implementation of the final proposal, what they can consider now is the fact that determining this new, increased reserve will likely require institutions to gather, archive, compute and make readily accessible significantly more data than what might currently be sufficient.

The following chart highlights the expected shift in data requirements:

Considering the data requirements and the impact it will have on ALLL methodologies, it would be prudent for institutions to begin taking a proactive approach today towards putting processes in place to meet these future needs and avoid a situation where the bank must be reactive – perhaps too little too late.

Many in the industry are turning to an automated ALLL solution, like Sageworks ALLL, to capture, archive and make readily accessible loan level detail. This not only helps a bank or credit union accommodate its data and calculation needs of today but also to proactively prepare for compliance needs of tomorrow.   

About the Author


Raleigh, N.C.-based Sageworks, a leading provider of lending, credit risk, and portfolio risk software that enables banks and credit unions to efficiently grow and improve the borrower experience, was founded in 1998. Using its platform, Sageworks analyzed over 11.5 million loans, aggregated the corresponding loan data, and created the largest

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