Qualitative adjustments in low-loss periods

Sep 2, 2016

Given all the hype and media attention, it could be easy for bank and credit union management to turn to their attention away from the ALLL under GAAP today and focus on the expected loss models that the FASB outlined earlier this summer.

In the meantime, there are several quarterly or monthly calculations that have to be completed under current GAAP. And for the calculation today, a common hurdle institutions face is stating conservative reserve levels in light of very low losses.

This problem is compounded when comparing to peer experience. For example, when a peer group’s average Nonaccrual Loans to Total Loans is 10 times higher than a specific institution’s, how can that bank justify a reserve that’s reflective of their actual losses?

In an on-demand webinar, Sageworks consultants identify a few steps that institutions can take towards the documentation of their allowance that may help institutions with low-loss history.

1)    Peer benchmarks. If auditors or examiners are looking for a particular number for the institution’s Allowance to Total Loans, use peer benchmarking data as supporting documentation. Some recommended benchmarks could include loss rates relative to total loans. These quantitative measures may help to differentiate the institution from peers and support the more qualitative assessments in the allowance, which could be questioned.

ALLL Performance Ratios

Example ALLL Performance Ratios

2)    Defensible qualitative components. In an August 2016 poll, more than half the respondents indicated that 10-30% of their allocation comes from qualitative adjustments, and 41 percent said 30% or more of their allocation comes from qualitative adjustments. Since auditors and examiners expect adjustments to be “comprehensive, well-documented and consistently applied”, the qualitative and environmental part of the calculation needs to be grounded in documentation. One way to establish credible adjustments is to base them on a matrix, featuring consistent risk factors and consistent thresholds for each factor.

3)    Regression analysis. In an on-demand webinar, Forward-Looking ALLL, consultants reviewed the role that regression analysis can play in helping to justify qualitative adjustments. For institutions with sufficient data and modeling expertise, it can be another way to bolster Q Factors and answer questions like “How does a change in unemployment impact my loss experience?” or “What was the impact to my losses when I started writing C&I to a new segment?” If the institution can tie its loss experience to indices like unemployment in the lending area, national CRE delinquency or house pricing, adjustments will be footed in defensible data.

4)    Scenario building. Auditors and examiners look for consistency in the calculation quarter to quarter, but through scenario planning the institution can model the ALLL under different assumptions to find the methodology that makes the most sense and is most reflective of actual losses (as proven through backtesting). Maybe the lookback period should be made include 3 periods instead of 5, or vice versa? By modeling each scenario, the institution can see what impact the timing changes may cause if, for example, a high-loss year drops off.