While many in banking remain vaguely aware of CECL—the current expected credit loss standard—there’s a point to getting past the tongue-twisting name: It signifies one of the most profound revolutions in financial services in generations.
In fact, the industry is heralding CECL as the biggest accounting change in banking history. And as financial services organizations transition into it, they must determine their segmentation—that is, how they break their loan portfolio into pools to estimate their allowance for loan and lease losses (ALLL). Whether they make wholesale changes or conclude that their current segmentation is optimal for CECL, institutions will need to document how they arrived there.
In practice, institutions have commonly tinkered with their loan pools over the years based on input and guidance from regulators and auditors (particularly during the financial crisis era of high-default portfolio portions). As such, the CECL transition offers a rare chance to revisit the portfolio with a fresh perspective. Keep these points in mind as you approach your CECL segmentation.
How granular is good enough?
The CECL standard states: “Segmentations or pools should have similar risk characteristics. These pools should be as granular as possible while maintaining statistical significance”(emphasis added). This balance between granularity and significance (in terms of pool size) represents a key friction point in segmentation elections. In most cases, finding the balance will require a blend of art and science.
As you consider risk characteristics that may warrant portfolio breakouts, you could easily dig deeper and deeper into the details. But with each additional filter added, each loan pool becomes smaller and as a result risks lacking significance. Remember: There is no “right” way to determine this balance. Each institution must weigh these decisions for itself.
Segmented portfolios, data controls
As noted above, there is likely no shortage of data fields that institutions could use to define risk characteristics for segmentation purposes. However, banks must mind the controls around each of these data points, as the end result of the allowance estimate is a financial statement entry.
For example, many institutions have noted that metrics such as loan to value (LTV) and borrower debt service coverage ratio (DSCR) can serve as important indicators of credit risk—and so consider using them as segmentation metrics. While intuitively this makes sense, such metrics rely on data points such as collateral values and borrower financial information. And institutions may lack adequate controls to ensure the accuracy and timeliness of those data points. It would likely be preferable to utilize credit risk grades/ratings as the segmentation metric. Here’s why: These ratings systems should already have LTV and DSCR metrics baked into them.
Mind the ‘counts’ in portfolio segments
With the size of each portfolio segment, consider not only the dollar size but also the units or “counts” of loans. The segmentation eventually serves as the format to apply an institution’s loss rate methodologies, qualitative adjustments and forecasts. Since default and loss events represent the “raw material” of most loss rate methodologies, having enough observations is critical.
For example, if we try to pinpoint a “lifetime” probability of default (PD) for a particular loan type, we may follow groups of loans (or “cohorts”) for a time period equal to our average life expectation for the segment. We could then observe how many loans experienced a default event, which you could define by payment delinquency, a move to non-accrual status, etc. This information could determine your expectation for a lifetime PD—but what happens if we over-segment our loan portfolio down to pools with low-loan counts? That increases the chances of getting “weird” results that don’t make intuitive sense or pools with little to no default or loss events, yielding a loss rate of zero.
Starting the segmentation sequence
You can begin the search for the “right” segmentation of the portfolio via the federal call code. This doesn’t mean that the granularity of the existing call report codes will suffice for all institutions, but the call code represents a common starting place that virtually all institutions already track (with the notable exception of credit unions). When they base segmentation on call code, institutions may find it easier to utilize aggregated peer data in segments, with little or no loss experience due to how call reporting is standardized.
No single “right way” solves the challenge of segmenting the loan portfolio for CECL. By carefully considering your institution’s loan portfolio and the concentrations within—and keeping the above points in mind—you can move toward a smooth transition to CECL , which with the right focus and diligence could ultimately stand for “clear excellence in credit logging.”