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Allowance for loan and lease losses: Steps to reduce subjectivity

March 1, 2013
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Sageworks recently hosted a dinner and discussion event, “The Allowance for Loan & Lease Losses: How to Justify Change & Remove Subjectivity.” Held in Houston, Texas, the event featured Sageworks consultants and Briggs & Veselka’s David Munn. This guest post by David Munn outlines his presentation at the event, which addressed reducing subjectivity in the ALLL.

How to Reduce Subjectivity from Qualitative Risk Factors

By David Munn

David Munn

Given my background as a 28 year bank examiner, I’m able to provide feedback from an examiner’s viewpoint. There are two things to keep in mind regarding examiners. First, examiners (generally speaking) are numbers people. Second, as government employees, they appreciate policies, procedures and/or protocol. So, while many field examiners are not comfortable with the subjective nature of qualitative risk factors, they have learned to adjust, just as they would to any new policy from Washington.

Qualitative risk factors refer to those items management should consider when estimating credit losses and are detailed in the 2006 Interagency Policy Statement on the ALLL. To reduce the subjectivity inherent in a typical ALLL model, I recommend a six step process. Again, the model has become subjective as a result of using qualitative risk factors.

Step 1: Develop “drivers” or variables for each of the nine qualitative risk factors detailed in the 2006 Policy Statement. These variables are the coefficients within a typical model that are assigned a numeric value that essentially “drives” the ALLL model and produces the end result. While there are several examples, the important thing is to make sure that they are representative of your portfolio, customer base and lending environment.

Step 2: Create a process that considers change and where modifications are applied consistently. The ALLL model should be fluid in that changes are identified and incorporated into the model easily.

Step 3: Use current information. Most of the data relevant to an ALLL model is susceptible to change at least monthly. An ALLL model is only required to be modified quarterly to mirror call report filings.

Step 4: Provide directional consistency. Remember that examiners are numbers people. When charge-offs, economic data and watch list reflect deterioration – the ALLL, as a percent of total loans, should be increasing.

Step 5: Use correlation analysis, et al, when possible. The best way to reduce subjectivity in the process is to turn it into an analytical process. This generally requires plenty of data. But correlation analysis, statistical analysis and trend analysis appeal to examiners.

Step 6: Back test the model periodically. A lot of time and resources go into the ALLL model. Testing assumptions from time to time is a sound way to see if existing views are accurate.


For updated regulatory guidance, questions, discussions or latest news on the allowance for loan and lease losses, join the LinkedIn group: ALLL Forum for Bankers.

David Munn is the Business Advisory Services Director at Briggs & Veselka Co. Mr. Munn has over 28 years of service with the Texas Department of Banking and participated in over 550 examinations of institutions ranging in size from small community banks to regional banks with assets in excess of $60 billion. He can be reached at 713-667-9147 or [email protected].


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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