Read this if you are a financial institution.
Have you ever noticed how a countdown seems to want to pick up speed the closer you get to the finish? 10, 9, 8… The chanting gets louder, and excitement builds with each passing number: 7, 6, 5… For me, the adrenaline really kicks in below 5: at 4, 3, 2.
Whether it’s excitement or anxiousness you’re feeling as CECL adoption approaches, staying focused on the remaining decisions is vital to your success. One of those decisions—and a frequently asked question in recent discussions with 2023 adopters—is about implementing Q factors (qualitative adjustments). So let’s take a look at some key considerations for Q factors.
10, 9, 8…
The role and nature of Q factors are to adjust for certain things you know or think will be different about losses in the future than they were in the past. They are also intended to be transitory. Like a scientific hypothesis, each Q factor adjustment exists until it bears out and is picked up in your primary model output, or it doesn’t. Either way, the need for that particular adjustment would presumably end. The one adjustment that most often comes to mind is economic forecasts, a new component of CECL. Let’s assume you have a qualitative adjustment for an expected upcoming recession—depending on your chosen method, for instance, the difference in reserve calculated when using a more severe economic scenario, or using historical losses only from a time period that was recessionary. Regardless, one would not expect this to be a permanently held adjustment—at some point, the recession happens, or it does not, and the adjustment may no longer be warranted. Categories of Q factors give us a place to start thinking through what specifically is different or what is changing, whether it is something broad, like economic forecasts, or something specific like new products or geographies in which you lend money. The key here is that it is something not accounted for already in the design, structure, inputs, or output of your core model.1
7, 6, 5…
Effectively implementing Q factors requires you understand how your segmentation (pools for collective analysis) and method selection affect your opportunity to make and support adjustments. What does this mean?
Segmentation under CECL requires grouping loans based on similar risk characteristics. This is inherently about behavior that has to do with risk and loss. Grouping all first lien residential mortgage loans together is a common one. If you have a big enough portfolio with enough historical loss history to support it, you may be able to further sub-segment that pool by geography (region, state, county), or by nature of use (primary residence, vacation home, or investment property), or by collateral lien position (first lien, junior lien). You’d want to do this if the inherent risk of loss you face is different. This loss risk difference could be a matter of customer behavior, or a difference in market conditions.
But what if, through the model development stage, you realize (or are told) that you don’t have enough data or big enough pools to model effectively at your preferred level of segmentation? Or, what if the method you chose comes with a level of segmentation already built-in?
4, 3, 2…
Let’s stick with residential mortgages. If you’ve accepted a higher level of segmentation, for example at the product level (residential mortgages) or even higher at the call report code level (1c), you may be thinking you have lots of leeway to make qualitative adjustments for all the nuances underneath. Not so fast. Are there any other modelling choices you’ve made that may prevent you from doing that? What about the use external loan data?
A common modeling technique is to use peer data to help establish the mathematical relationship between historical losses and changes in the economy. Most peer data is only available at the call report code level, which means that all those nuances in your “1c segment” that you want to further adjust may already be embedded in your methodology because of the connection to peer data. This may be even further complicated by situations in which you have no say in more proprietary loan data sets model developers may be using to support their model design, assumptions, and formulas. External loan pool data is just one of many things to consider when evaluating when, and if there are opportunities to support a Q factor adjustment.
So what’s a responsible CECL adopter to do when considering how best to make qualitative adjustments?
First, understand that the intention is to account for changes and differences, which inevitably is a moving target. Adjustments and their related reserve dollars are not fixed, which is why they should be subject to re-assessment each time you update your CECL calculation. Second, if you’re using a vendor’s model, it is imperative that you know how, when, and to what extent loan data other than yours is or has been used, both in developing the model and in its on-going operation. Third, you need to understand, document, and think through the interplay between the segmentation you have and model design or other assumptions made along the way. Finally, if in the normal course of operation, you change any of those assumptions or inputs, it should trigger a re-evaluation of your Q factors to ensure they remain relevant.
Want help with your Q Factor approach? Are you struggling with CECL documentation or other elements of CECL?
No matter what stage of CECL readiness you are in, we can help you navigate the requirements as efficiently and effectively as possible. For more information, visit the CECL consulting page on our website. If you would like specific answers to questions about your CECL implementation, please visit our Ask the Advisor page to submit your questions.
For more tips on documenting your CECL adoption, stay tuned for our next article in the series. You can also follow Susan Weber on LinkedIn.
1If you haven’t already, I highly recommend that you read these sections of the accounting standards codification (ASC) 326, commonly referred to as the CECL standard: 326-20-55-4, 326-20-30-7, 326-20-30-8, and 326-20-30-9.