Q Factors and Their Future Under CECL
Under current GAAP, Q factors are a tremendous challenge. Currently, frameworks to evaluate qualitative factors vary across the industry. KPMG’s Ben Hoffman helped 2016 National ALLL Conference attendees understand how Q factors will be applied under CECL.
KEY TAKEAWAYS FOR Q FACTORS UNDER CECL
- CECL models will likely incorporate several of the current qualitative factors, but will likely require additional data points.
- The types of data needed for CECL is tied to your methodology and the unique needs of the institution.
- Under CECL, Q factors will be a tool people use to get a reasonable provision.
Leading practices are generally based on (1) Risk-level assessment framework or (2) Scenario analysis (or a combination of both).
Qualitative adjustments contribute a higher proportion of the total ALLL for financial institutions with limited loss experience or those with improving credit performance as the quantitative loss estimates do not sufficiently estimate the level of incurred losses.
There are a range of approaches used by banks and credit unions to determine severity of each risk factor and its magnitude of influence on the portfolio losses.
Banks and credit unions continue to enhance their documentation and methodologies to support regulator’s and auditor’s expectations to make the process structured, transparent, and repeatable with a sufficient level of precision.
CECL models will likely incorporate several of the current qualitative factors, but will likely require additional data points. Of the nine factors, those likely to be integrated into a CECL quantitative model are:
- Changes in lending policies and procedures
- Changes in international, national, regional, and local economic and business conditions (w/adjustments)
- Changes in the nature and volume of the portfolio
- Changes in asset quality (w/adjustments)
- Changes in the value of underlying collateral for collateral-dependent loans
The remaining Q factors are likely to remain unchanged from the current incurred loss model as they are difficult to incorporate into specific quantitative model assumptions on a recurring basis.
Things that are going to change significantly under CECL will require a lot of thought and a lot of explanation. Small changes in some factors may have big impacts so there will have to be sensitivity studies. How you document in a way that gets board members to sign off will be a challenge. To what extent will you need to change your process? No one knows. You will go through a lot of resources to get to what the effect will be, what on day one will be acceptable. Getting a better understanding of how the different parts fit together will be something that develops over time.
Will you need a lot of data? Clients want to know what types of data will be needed, but that’s tied to your methodology and the unique needs of the institution. Under each scenario, what data will you need? That will drive your data needs.
When you quantify, values are very unstable over time. Sometimes a certain factor might have a big impact and other times not – different factors will have different impact for different institutions. Some people have already included the Q factor quantities in their modeling, building it into the model. But if you’re doing a lot of them, be sure they make sense in totality. Under CECL, Q factors will be a tool people use to get a reasonable provision.
Click to view the MST Talk with Ben Hoffman as he discusses Q Factors and their future under CECL in this MST Talk filmed at the National ALLL Conference in 2016.
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About the Speaker Ben Hoffman | KPMG | Director, Credit Risk Group
Ben specializes in credit risk analytics for both commercial and retail portfolios. In this function, he has assessed and enhanced credit risk measurement frameworks and processes to help financial institutions better measure and understand their portfolio’s credit risk. Ben has extensive experience in Allowance for Loan and Lease Losses (ALLL), Basel II parameter estimation, stress testing (both CCAR and DFast) and data analytics. Prior to joining KPMG, Ben worked at Deloitte and the Bank of New York in a credit analytics role.