How to Leverage CECL to Improve Operations

As the December 2019 deadline for the Current Expected Credit Loss (CECL) standard draws nearer, financial institutions need to think beyond compliance. Meeting all the rules for the new accounting paradigm for expected credit losses will surely be a time‐consuming and costly challenge, but it can also yield significant advantages for your business.

Indeed, operationalizing CECL, if done properly, can add value by enhancing risk-based pricing for products, boosting risk‐adjusted profitability, optimizing portfolio/product mix and even helping you make more informed acquisitions or divestitures. Moreover, credit risk models developed for CECL can help management better understand what risks are worth taking and gain greater insight into the nature and performance of their loans and asset portfolios.

The clock, of course, is now ticking. All public business entities (PBEs) that are SEC filers in the US need to comply with CECL by December 15, 2019. PBEs that are non‐SEC filers and all other entities in the US must comply by December 15, 2020, and December 15, 2021, respectively.

To drive real value and to move beyond a simple compliance approach, firms must first figure out how to leverage the CECL use test.

CECL Use Test Requirements

Each financial institution that falls under the scope of CECL must include model outputs, internal reporting metrics and disclosures of its day‐to‐day risk management activities. Specifically, CECL will push banks to refine their origination (including estimating cost of credit); underwriting; portfolio analysis; and risk measurement, monitoring and reporting processes.

To accomplish this, the bank's lines of business (LoBs) will need to develop advanced metrics to better understand and monitor the credit quality of their portfolios - including individual loans, whenever possible. The LoBs must attribute their expected losses to specific macroeconomic factors, portfolio‐specific factors and loan-level factors. These factors should lead to improved risk‐based pricing, higher risk‐adjusted profitability and lower expected losses.

What's more, every CECL‐compliant financial institution must be able to determine the reasons for changes in the estimate of expected credit losses from one period to another. The goal for every firm should be to use CECL for improved credit risk appetite setting and enhanced capital allocation for granular‐level portfolios and large individual loans.

Getting More from CECL

Required disclosures under CECL are not meant to be merely a check‐the‐box compliance exercise. Smarter firms will fully leverage these disclosures and other internal metrics to assess the credit risk inherent in their LoBs, portfolios and/or individual loans with greater precision.

For example, suppose a specific retail LoB originates several new loans that are mostly prime, but that also include a much smaller percentage of near‐prime loans. After assessing the overall initial credit quality of this new portfolio and the existing underwriting criteria, the LoB buckets all the loans into the prime segment because, on an aggregate basis, the new portfolio exhibits prime characteristics under the bank's credit ratings system. The modeling team subsequently estimates the expected losses for this portfolio, but then determines that losses are higher than its peers with similar portfolios and wonders why.

With deeper analysis, our sample bank finds that a small percentage of customers indeed had near‐prime risk characteristics - but were mistakenly labeled prime as part of the bigger portfolio. These near‐prime customers were offered a lower pricing at origination that was not commensurate with their risk.

Could the bank have lowered its initial estimate of net expected losses? Yes. Here is how:

The bank should have split the larger portfolio into two risk pools: prime and near‐prime. The loans should have been priced in accordance with individual customers' respective risk profiles, with higher prices for near‐prime customers. Higher prices could have offset some of the expected losses in the near‐prime segment, thereby lowering the net expected losses for the overall portfolio.

The bank should have realized it could mitigate future losses by capturing data at a granular level. Subsequently, it could then have spent more time scrutinizing the following CECL‐required data: reasons for impairments (pricing was not risk‐based); the number of positions that are in an unrealized loss position (loan counts and proportion of loans in prime vs. non‐prime segments); and the severity and duration of the impairments, for each segment.

What are the two key lessons from this exercise? First, create risk segments based on pre‐specified business and risk criteria to be able to conduct proper risk attribution. Second, high‐quality data is imperative - the bank could not have offered differentiated pricing for the two segments if it did not have granular loan‐level data.

Parting Thoughts

CECL can inform strategy - e.g., it enables a bank to lean on those business segments that provide higher risk‐adjusted returns and operate within their credit risk appetite, leading to improved capital allocation. A CECL bank can also revamp its operating procedures, so that all originations are examined with greater precision for credit risk.

What is the bottom line? Do not look at CECL as simply a compliance exercise. Leverage risk and performance insights to inform strategy, lending and pricing decisions. This will all lead to improved results.

Varun Agarwal (PhD, CFA) is a risk specialist in the Strategic Risk & Operations Practice at Grant Thornton, LLP. He provides advisory services to financial services clients and has more than 20 years of experience in risk and regulatory management.