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Video: How to justify and support your qualitative factors – Part I

Sageworks
April 11, 2014
Read Time: 0 min

Justifying and documenting qualitative and environmental factors in the allowance calculation is a common challenge for banks and credit unions. More than 50 percent of the bankers during a recent webinar pointed to qualitative and environmental factors as their biggest ALLL challenge. In part 1 of this video series on qualitative factors, we identify best practices and supporting documents that can be used to justify and document your external-looking qualitative factors. Additional parts of the videos will cover internal factors.

 

 

From the video

In talking to financial institutions about their use of environmental adjustments in their allowance calculation, a common challenge we hear is documenting and supporting those qualitative adjustments. There are nine standard qualitative adjustment factors that have been defined by regulatory guidance in the 2006 Interagency Policy statement. I think it’s useful to break those nine factors into two categories. One category is internally focused, meaning within the financial institution, changes in the loan portfolio, changes in management and other considerations. The other category is external looking.

Thinking about the external-looking factors, there are really three critical ones that are going to be a part of this process.

1. Changes in the national, international and regional conditions. This is something we often refer to as the economic factor. We see institutions using any economic data they can get their hands on to support these. But what is really critical is not so much the information that you choose to use but how you’re able to show the trends over time. So if you’re going to pick a particular data set or a particular metric, it is really going to be about showing that consistently and applying these changes consistently over time. The most common information I see institutions use is the Federal Reserve Economic Data (FRED). Within that data set, the unemployment rate is probably the most common metric that we see used.

2. Changes in the values of underlying collateral for collateral-dependent loans. This is market-specific data and this is probably mostly going to refer to real estate values. Any market-specific information that you can use (Case Shiller data or other local data) in terms of market values for real estate and other collateral is going to be critical, as is how well you apply that consistently from period to period over time.

3. The effect of other external factors on the level of estimated credit losses.  This is really a more generic catch-all factor, more specifically referring to changes in the legal, regulatory or competitive environment. We often caution institutions to use this one a little bit more carefully as, since it is a catch-all, we find it can be a little harder to support changes. However, if there is a meaningful change in your competitive environment or changes in the regulatory environment, particularly now with the changes coming from FASB in terms of moving from an incurred loss to an expected loss model, that may be something where you can actually adjust your loss rates based on these market changes. The challenge for the external factors is they are a little bit harder to document. You are going to want to be as specific as possible in terms of defining what the actual change is and what the effect would be on your loss rates.

Sageworks ALLL allows bankers to directly access and upload supporting documents from Federal Reserve Economic Data to enhance their qualitative factors.

About the Author

Sageworks

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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