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Guidelines for ALLL Qualitative Risk Factor Analysis

September 12, 2012
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The Allowance for Loan and Lease Losses (“ALLL”) represents one of the most significant estimates in a financial institution’s financial statements, as the appropriateness of these loss provisions is critical to an institution’s safety and soundness. Consequently, as the banking industry struggles to recover from the most significant economic downturn since the Great Depression, institutions face intensified regulation and scrutiny pertaining to their ALLL calculations. There are several overarching challenges in the estimation of the allowance for loan and lease losses process that financial institutions face with regularly.

Although the ALLL has many components, one stands supreme in difficulty – the determination of appropriate qualitative risk factor adjustments. Lack of specific direction on how these determinations are to be made provides management teams with tremendous leeway in manipulating their ALLL calculations; however, it also exposing institutions to significant regulatory scrutiny. Regulators want structure and consistency in this inherently subjective task, but a modern-day author, Toby Beta, wrote, “Subjectivity measures nothing consistently.”

Management can use the recommendations and suggestions below to help add objectivity and structure to this otherwise subjective task and appropriately justify their assumptions.

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  1. Follow Interagency Guidance 

The nine qualitative factors recommended in the 2006 Interagency Policy Statement on the ALLL should be considered when an institution estimates credit losses, and should be considered a part of the institution’s overall standard process of review.

  1. Create a standard process of review

Creating an institution-wide standard process of review regarding proper procedure and application of qualitative risk factors will ensure consistency and limit the amount of subjectivity.

  1. Utilize current market information

Considering current market information, economic trends and events within an institution’s lending footprints can add objectivity and structure. These environmental factors should be reflected in an institution’s quantitative adjustments, mirroring any recognized improvement or decline.

  1. Ensure directional consistency

Ensuring that determinations are always directionally consistent with credit quality trends is critical. Directional consistency validates that as drivers and factors change rate directions, an institution’s qualitative rates change directions as well and in accordance with the proper correlation to the driver and factor.

  1. Conduct correlation analysis

Correlation analysis enables management teams to measure the strength of the relationship between two variables: how well changes in one variable can be predicted by changes in another. By utilizing known historical data to calculate the correlation coefficient, management may forecast future data that may be used to appropriately support qualitative adjustments.

  1. Use back-testing as a method of validation

The use of back-testing allows management to test current assumptions or adjustments against actual historical data, in an effort to use the results to add credibility when making those same assumptions or adjustments today. After all, as renowned NYSE trader William Gann taught, “The future is but a repetition of the past.”

  1. Utilize a probability of default model

Utilizing a quantitative credit risk model, such as the Probability of Default Model, may also prove helpful to management in adding objectivity to their qualitative factor adjustments, by providing a method to express a quantification of the prevailing source of risk for banks – credit risk.


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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