In late 2019, the Financial Accounting Standards Board (FASB) provided relief to private companies and smaller reporting companies by deferring the effective date of the new Current Expected Credit Losses (CECL) standard (ASC 326) to annual financial reporting periods beginning after December 15, 2022 (2023 for calendar year-end companies). The adoption of the new standard provides significant challenges, making it imperative that private companies use this additional time to ensure they are prepared for the adoption of this complex standard.
One of the most challenging aspects of adopting the new standard is the need to collect and analyze historical credit loss information. Entities are required to maintain historical credit loss information on an aggregate basis for financial assets that share similar risk characteristics. Entities are also required to adjust historical loss experience for both current conditions and for reasonable and supportable forecasts of future events.
Some factors that might have material impact on management’s estimate of the credit losses include the nature of the financial assets, borrower’s credit rating, prepayment rates, value of underlying collateral, regulatory environment, and changes and expected changes in economic conditions (e.g., unemployment rate, home prices, interest rates).
In certain instances, internal historical loss data may be incomplete or not be reflective of a full credit cycle. In such instances, management will need to supplement their internal historical loss data with external data, and they may need to make adjustments to calibrate these loss rates to the specific risk characteristics of the company’s financial assets.
While many larger institutions that adopted the new standard as of January 1, 2020 were able to develop in-house credit models, this is not always a viable option for smaller institutions. A number of external vendors have developed technology platforms that can be utilized to develop management’s estimate of the credit losses by utilizing both internal and external data. The appropriate selection of an external vendor model at an early stage in the adoption process is a key milestone. Companies need to carefully evaluate if a vendor model provides the right fit for their specific portfolio of financial assets. Additionally, vendor models generally need to be “calibrated” to reflect the company’s specific credit risks. While analyzing and documenting the differences between the company’s financial assets and the vendor’s database can develop a reasonable calibration, the process can be complex and time consuming.
For non-financial companies without an extensive portfolio of financial assets, it may be appropriate in some circumstances to utilize spreadsheets to determine management’s estimate of credit losses.
Backtesting and Parallel Runs
The new standard represents a significant change in determining reserves and therefore it is essential that management allows sufficient time to perform backtesting and parallel runs on the expected credit loss models. Depending on the company’s assets, there may be several different types of models that need to be implemented and require validation. In some instances, companies may need to perform multiple parallel runs prior to adoption in order to fine tune models and ensure that any unanticipated results are appropriately addressed. Additionally, the execution of timely parallel runs is critical to ensuring that the expected impact can be appropriately communicated to the company’s investors and other stakeholders.
How MorganFranklin Can Help
With the benefit of additional time for private companies to implement the new standard, we encourage them to understand the new requirements, assess the impact, and determine adoption strategies to drive better business decisions. At MorganFranklin, we have dedicated and experienced professionals who understand the challenges private companies may face in assessing and adopting the new standard. We take a thoughtful approach to adoption and ongoing compliance, which includes analysis of data requirements and appropriate technology solutions, consideration of necessary business process controls and procedures for sustainable reporting, and determining the right information to meet disclosure requirements ahead of the adoption deadline