‘What will happen to Q factors?’

Want to know how to develop qualitative factors, or Q factors, under CECL, the current expected credit loss standard? You’re not alone.

Q factors – more specifically, “What’ll happen to my Q factors under CECL?” is a popular topic among bankers, especially those with a 2023 CECL implementation date, according to Zach Englert, a CECL Consultant with Abrigo’s Advisory Services.

Many financial institutions in recent years have relied on qualitative factors for a larger percentage of their reserve when calculating the allowance for loan and lease losses (ALLL) under the incurred loss method as good credit quality put downward pressure on the quantitative portion of the estimate. Understandably, banks and credit unions transitioning to CECL in 2023 want to know how usage of Q factors under the new standard will compare with their current practices. Another common question from institutions, Englert says, is “What should the percentage of quantitative to qualitative be in the allowance under CECL?

Step 1: Understand the quantitative analysis

While there’s no universal answer to either question because banks and credit unions and their loan portfolios and loss experiences can differ so much from each other, CECL experts agree that the first step to applying Q factors under CECL is a solid understanding of the quantitative side of your financial institution’s CECL calculation. After all, said Garver Moore, Abrigo Managing Director, “The purpose of the qualitative factors is to address what’s not in the losses expected from the quantitative baseline analysis.”

“When we talk about Q factors with our clients, I think about three questions that they’ve got to answer” in using them, saidGraham Dyer, Partner at Grant Thornton, LLPthe adjustment quantitatively appropriate?, during Abrigo’s recent ThinkBIG Conference. He outlined these questions as follows:

  1. What is not captured by the model that requires you to make this adjustment?
  2. Is the adjustment directionally consistent?
  3. Is the adjustment quantitatively appropriate?

“Tell me what is not in that model — why you need an adjustment in the first place,” Dyer said.

Some methodologies necessitate the use of more Q factors than others, said Regan Camp, Managing Director at Abrigo. “It really depends on the type of methodology you’re leveraging.”  The COVID-19 pandemic illustrates this. Quantitative models incorporating loss-rate forecasts based on unemployment estimates were complicated by the impact of government stimulus payments and other factors.

Documenting the reasoning behind adjustments tied to qualitative factors is important. “We continue to see clients making adjustments for things that we say, ‘Can you show me why that’s not captured by your model?’ And it’s not something they’ve considered,” Dyer said. “So building that logic into the process is going to be important.”

Whether the adjustment is up or down also needs to make sense, as does the amount of the adjustment. This last issue can sometimes be difficult for financial institutions to address, Dyer said. “Sometimes it’s helpful to have quantitative methods to try to put boundaries around those things as much as possible,” he said. “You at least have to tell me not just why I adjusted but why I made it this much.”

Experts also suggest scratching the existing numerical Q factor adjustments an institution is using in the incurred loss model.

Some financial institutions might say, “’Here are my Q factor adjustments. Do I keep that and start from there?’” said Gordon Dobner, Partner in BKD’s National Financial Services Group, during the recent ThinkBIG Conference. “I think you completely scratch it. It should be a brand new consideration. You can’t really say, ‘In incurred, I had 75 basis points, so that’s my starting point.’”

Dyer agreed. CECL is a “wholly different approach” than the incurred loss methodology, he said. “I can’t see why you wouldn’t start from a pretty blank sheet of paper.”

A framework for Q factor adjustments

A qualitative scorecard for the allowance provides a framework that enables the financial institution’s management team to determine reasonable and supportable Q factor adjustments to the quantitative baseline estimate. The scorecard is a reliable and consistent mechanism that can be backtested against subsequent performance, too.

Here’s how scorecard development works:

  1. Review the quantitative model(s)/methodology that will be used to calculate the baseline loss estimate.
  2. Identify quantitative metrics that assist in framing various risk scenarios, from minor to major.
  3. Leverage peer analysis against historical loss experience to determine a high- and low-mark estimation framework.
  4. Identify appropriate scorecard frameworks to specific circumstances and institution preferences.
  5. Create a qualitative scorecard for each allowance pool based on the broadly or uniquely identified selections (or a combination of both).

Currently, many institutions use the same Q factor for the entire portfolio, but 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 regulators.

A qualitative adjustment scorecard can simplify the quarterly process of developing and documenting Q factors, especially if the scorecard can be interconnected with the financial institution’s CECL model.

“To assess a Q factor, you have to know what’s in the quantitative model and the limitations of it,” said Moore. “A qualitative scorecard, therefore, should ensure that the qualitative aspects are not ignorant of the quantitative aspects. They should ‘talk’ to each other.”

This is also an advantage of the scorecard when it comes to financial reporting from period to period. As credit losses associated with Q factors are recognized and the quantitative portion of the allowance is updated, the concomitant qualitative factor adjustments drop off the scorecard.

Conclusion

Q factors aren’t going away under CECL. And while it’s impossible to provide a blanket assessment of how every institution’s Q factor adjustments will compare to those under the incurred-loss method, it’s a certainty that auditors and regulators will be focused on understanding the reasoning behind adjustments, as well as how the adjustments were determined. Using a qualitative scorecard can make this process easier and more consistent for financial institutions.

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