The Benefits of Early CECL Adoption

FASB’s Accounting Standards Update (ASU) No. 2016-13 (more commonly referred to as CECL) was released earlier this year in June. At first look, the implementation dates for CECL are so far out that this project may not rank very high on a management team’s to-do list.

• Public business entities (PBEs) that are SEC registered have a regulatory reporting effective date March 31, 2020.
• Other PBEs March 31, 2021
• Private companies December 31, 2021
• Early application for all entities December 15, 2018

However, there are potential benefits to moving CECL up in priority and adopting the accounting standard early.

Segregating Vintage Data Can Prevent Overstating Future Losses

Vintage analysis is a key component of a solid CECL model and most community sized institutions do not utilize it as part of current ALLL calculations. What is missed by not studying vintages is easily demonstrated by looking at mortgage loans made from 2006 - 2010. Loans made from 2006 - 2008 showed poor performance compared to loans made after 2009. We all know this story by now. Underwriting standards tightened, LTVs declined, housing values hit bottom, etc. By segregating the poor vintages from the rest of the portfolio, a lender can significantly reduce required loss reserves by applying the higher expected loss rates to the “bad” loans and using lower expected losses more appropriate to the rest of the portfolio. Segregating by vintage provides the analyst insight required to decide which key performance indicators (LTV, housing index, FICO) will be used to further define the portfolio into even smaller pools providing a highly refined view of required reserves. Banking industry trade groups have warned of the potential for CECL to increase ALLL as much as 30% for community institutions. This data storing and refining process is the only way a financial institution can optimize ALLL calculations.

Refined Data is a Strategic Advantage

The expected loss methodology is easily more complex and more data-dependent than the incurred loss methodology in that it promises to involve significant resources from across the entire organization. If a financial institution goes to all of this work and only uses the information they’ve complied to satisfy a regulatory checkbox, then they’ve missed the larger benefit. Data-driven support of probability of default that gets further refined with every month of new data and is added to the model could be as much of a strategic tool as it is a regulatory tool:

Financial institutions that are early adopters of CECL will have a real advantage in implementing when they choose to instead of when they have to. And if their data harvesting yields key strategic results, they may just choose to adopt early.