ACL Model Validation
Part III: Qualitative Adjustments
By: Peter Warmenhoven, Consultant
As previously mentioned in Part 2 of this ACL Model Validation series, financial institutions have recently transitioned to the new Current Expected Credit Loss, or “CECL” method of estimating the Allowance for Credit Losses (“ACL”). While much about the CECL method is unfamiliar, and while ACL calculation models vary, there are a few basic similarities with the previous incurred-loss method. First, historical loss information still provides the starting point and figures heavily into the “quantitative” calculation of an institution’s ACL.
Further, the CECL method still incorporates adjustments for qualitative and environmental factors into the ACL calculation, to adjust for how current or forecasted conditions differ from historical loss experience. The purpose of qualitative factors is to adjust for what is not in the historical loss analysis. An important difference with CECL is that management should apply a forward-looking thought process when evaluating these criteria. Q-factor adjustments should address the differences in the current environment, and the current or expected conditions that indicate that future loss rates will be different than losses incurred in the past.
The Interagency Policy Statement on Allowances for Credit Losses (revised April 2023) provides guidance relating to factors that should be considered when adjusting historical lifetime loss information:
Management should consider the need to qualitatively adjust expected credit loss estimates for information not already captured in the loss estimation process. These qualitative factor adjustments may increase or decrease management’s estimate of expected credit losses. Adjustments should not be made for information that has already been considered and included in the loss estimation process.
Management should consider the qualitative factors that are relevant to the institution as of the reporting date, which may include, but are not limited to:
- The nature and volume of the institution’s financial assets;
- The existence, growth, and effect of any concentrations of credit;
- The volume and severity of past due financial assets, the volume of nonaccrual assets, and the volume and severity of adversely classified or graded (i.e. substandard or worse) assets.
- The value of the underlying collateral for loans that are not collateral-dependent;
- The institution’s lending policies and procedures, including changes in underwriting standards and practices for collections, write-offs, and recoveries;
- The quality of the institution’s credit review function;
- The experience, ability, and depth of the institution’s lending, investment, collection, and other relevant management and staff;
- The effect of other external factors such as the regulatory, legal and technological environments; competition; and events such as natural disasters; and
- Actual and expected changes in international, national, regional, and local economic and business conditions and developments in which the institution operates that affect the collectibility of financial assets.
The new guidance is similar in many respects to the factors identified in the Interagency Policy Statement on the Allowance for Loan and Lease Losses dated December 2006. The guidance also observes that this list is not comprehensive. Under CECL, qualitative adjustments may differ on a pool-by-pool basis. “Depending on the nature of the asset, not all of the factors may be relevant and other factors also may be relevant and should be considered,” according to a 2019 Frequently Asked Questions (FAQs) on CECL by the regulatory agencies.
With good credit quality and low loss rates prevailing in the industry over the last 10+ years, many financial institutions have relied heavily on qualitative adjustments as a key component of their reserve. Just how much of the ACL should be attributable to qualitative adjustments? The answer is frankly “It depends” and will be different for each institution. Management must identify and adjust for what is missing or different from the quantitative calculation answer the question, “What’s not captured in our model that warrants an adjustment?”
The Interagency Policy Statement provides one such example, relative to economic and business conditions affecting an institution’s loan portfolios: “An economic factor for current or forecasted unemployment at the national or state level may indicate a strong job market based on low national or state unemployment rates, but a local unemployment rate, which may be significantly higher, for example, because of the actual or forecasted loss of a major local employer may be more relevant to the collectibility of an institution’s financial assets.”
A different type of qualitative adjustment may be needed for institutions that use peer group loss rates as a basis for their methodology, to account for the variances in the underlying portfolio. If peer group historical loss rates are used in place of internal data, how might actual loss rates differ? And as most peer data is only available at the call report code level, any differences in pool segmentation might call for appropriate Q-factor adjustments.
Auditors and regulators will be focused on understanding the reasoning behind adjustments, as well as how the adjustment amounts were determined. The key to justifying Q-factor adjustments is for management to properly document and support its rationale. A good starting point for management to document its adjustments is a simple spreadsheet or scorecard that sets out the adjustment in each loan segment for each of the above qualitative factors. The amount of any adjustment should be clear, along with a comparison to the amount of adjustment at the previous calculation date.
To help justify the amount of each adjustment, some institutions attempt to build consistent logic into the process. These institutions simplify the process by assigning a standard numerical adjustment value to factors depending on varying degrees of risk.
For example, and strictly for illustration purposes:
|
Risk Level |
Bps Adj. |
| High | 25 |
| Moderate | 15 |
| Low | 5 |
| None | 0 |
While this specific example may not be appropriate for every institution, a consistent set of standard adjustment values adds transparency to the adjustment process and eliminates the need for examiners to question why one adjustment was say, 15 bps while another adjustment was 17 bps.
Documenting the reasoning behind adjustments is an often-overlooked but crucial priority. Vendor models typically have features that allow management to select relevant qualitative adjustments and comment as to their reasoning. Even with the best modeling software, however, it is still management’s responsibility to document their rationale as to why, and to what extent qualitative adjustments are needed. Whether this is done within the framework of a vendor model, a scorecard spreadsheet or other written document, management’s analysis and judgment must be adequately supported. As environmental conditions change, management must evaluate whether Q-factor adjustments made previously remain valid. This is an ongoing process requires a dedicated amount of management’s time and attention at each ACL measurement date.
In 2023 and 2024, our ACL Validation service assisted numerous clients in validating their CECL methodologies and provided ongoing guidance for the ACL process. As the “new” accounting treatment approaches its first anniversary for many community-based financial institutions, some may not have undergone a regulatory review since its implementation, lacking regulatory insights into the ACL process. An ACL Validation will help identify areas for improvement either prior to or in support of regulatory feedback.
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