The first component of the ALLL calculation consists of generating a historical loss view. This consists of first classifying loans into two different categories, ASC 450-20 (FAS 5) and ASC 310-10-35 (FAS 114), contingent upon their performance.
Institutions must select a measure of loss, which consists of peer analysis, historical loss, migration analysis and PD/LGD. Different data requirements are needed for each methodology, and some derive a more granular analysis than others.
The goal of the quantitative calculation portion of the ALLL is to determine what financial institutions’ historical loss experiences have been, and to then derive a basis point for the estimate of within their current portfolio.
Insights from FASB’s Recent Q&A on CECL
On July 17th, 2019, the Financial Accounting Standards Board (FASB) agreed to formally propose extending the effective date of the Current Expected Credit Loss (CECL) standard to January 2023, for all but the larger SEC filers. This highly anticipated announcement of additional relief for most understandably overshadowed another document the FASB also issued that same... Read more »
Rethinking Risk Ratings Ahead of CECL
Depending on the methodology elections an institution makes under the current expected credit loss (CECL) model, risk ratings can be an absolutely critical input for loss rate calculations. This is especially true for migration analysis. But even more broadly, risk ratings play an essential role in understanding, measuring and limiting credit risk to the institution. Consequently, the risk rating policy that defines measurement criteria, timing and use-cases is an important document under both incurred and expected loss models.
What is the discounted cash flow (DCF) methodology?
A discounted cash flow methodology in the context of ASU 2016-13 (Topic 326/CECL) is one way to estimate credit losses. Discounted cash flow (DCF) methodologies utilize a bottom-up approach—meaning they model expected cash flows on a loan-level basis and aggregates results at the pool-level.
ASC 310-10-35 (FAS 114)
ASC 310-10-35 (FAS 114) loans, or “impaired” loans, are calculated individually, as the parameters of each loan and its measure of loss will be unique. There are various ways to value these loans, including fair market value of collateral, net present value of future cash flows, and loan pricing.
Peer data can be useful for de novo institutions, but should no longer be used when institutions have a substantive amount of data on their portfolio to perform a historical loss methodology.
The use of “what-if” scenarios gives financial professionals the ability to determine the impact of one particular component on the overall calculation. A “what-if” scenario consists of running the ALLL calculation under normal parameters, then altering one variable while keeping the rest of the calculation constant to examine the impact of that variable.
Backtesting refers to retroactively validating the accuracy of an institution’s methodology. This consists of pinning the ALLL calculation against actual credit losses to determine the degree of variance. Having a consistently accurate ALLL can serve as an invaluable tool in validating your methodology and defending it to regulators.
Measures of Loss
When calculating the FAS 5 portion of the ALLL, various methodologies can be used. These consist of peer analysis (for financial institutions that may still be in de novo status), historical loss, migration analysis and PD/LGD.
ASC 450-20 (FAS 5)
ASC 450-20 (FAS 5) loans are deemed as “performing” loans, and are pooled as opposed to being examined individually. Various measures of loss can be used to determine the expected loss from ASC 450-20 (FAS 5) pools. Institutions who are diligent about segmenting these pools can gather insight into the performance of each pool.
ASC 450-20 (FAS 5)
Before accounting for expected credit losses, institutions must first determine which loans belong in FAS 5 (ASC 450-20) status and which belong in FAS 114 (ASC 310-10) status. It is important that institutions are consistent with how they classify their loans, as inconsistency can drastically change the end ALLL figure and draw scrutiny from regulators.