Current Expected Credit Loss (CECL) is Delayed: What Now?
By Ryan Siebel, Partner, Assurance Services
In October, the Financial Accounting Standards Board (FASB) unanimously approved a delay in the change in accounting for credit losses known as CECL (current expected credit loss). Implementation for smaller public companies and small banks is now delayed until January 2023, which gives those institutions more time to prepare for the coming change. There are lessons to be learned here, including from big banks that have already begun to implement the new standard, and now is the time to obtain that knowledge and prepare your systems to collect more complete historical data needed for CECL.
Overview: What is CECL?
There has been much discussion in the financial services industry on the impact of the forthcoming accounting requirement (ASU 2016-13) to use the CECL model in estimating future losses on financial instruments. This is not surprising, given the importance of lending to banks and other financial institutions and the relative size of loans and other financial assets on the balance sheets of such financial service industry companies.
However, the impact of ASU 2016-13 is not restricted to banks and other financial institutions. The new standard applies to all a company’s financial assets, with few specific exceptions. Financial assets subject to the new standard for estimating credit losses include trade receivables, loans, held-to-maturity debt securities, lease receivables and financial guarantees.
It will be imperative for every company that holds these assets on its balance sheet to determine the impact of the new standard’s adoption on its financial statements.
The New Standard: Accounting for Expected Losses
The CECL model’s main change from current accounting rules is a requirement to incorporate forward-looking information in estimates of credit losses, hence the word “expected” in the model’s name. Companies will be required to forecast the total expected losses on their total accounts receivable, even those that are not past due at the reporting date. The forecast is based on historical information, current information and reasonable and supportable forecasts.
Financial assets with similar risk characteristics (e.g., all current accounts receivable from domestic entities, all past due accounts receivable from foreign entities, etc.) should be pooled to determine the estimated loss. The CECL does not require a specific methodology for developing a forecast of expected losses, the length of the forecasting period or the amount of precision required. As such, judgment will be applied in estimating the overall expected loss.
Many companies currently use a matrix of percentages to reserve for accounts receivable based on aging categories. While these matrices may still be used under CECL, the percentages used will depend on both historical loss data and reasonable and supportable forecasts of future losses. Therefore, many companies will be applying a reserve percentage for credit losses on their current receivables for the first time under the new standard.
The CECL model applies to loans, including loans to officers and employees, so companies will have to estimate their projected losses on those assets as part of the overall adoption of the standard. ASU 2016-13 does not apply to receivables between entities under common control.
Why Data Matters Now
CECL is highly dependent on how your historical data has been captured. The delay offers small companies an opportunity to change how they handle data around credit losses in a way that will be beneficial for the eventual CECL implementation. Turning your attention now to these efforts is important. Gaps should be filled in and standards should be set for how to accurately capture and report credit data going forward, so that all the information will be available when the time comes. Consider merging systems for more complete data capture or hiring a professional who can identify gaps and implement fixes in data collection. Credit experience data that isn’t captured accurately and completely now may be data you can’t go back and find. This delay affords a great opportunity for a cleanup and fresh start from a data perspective.
Further, smaller financial institutions can now look to understand from bigger banks what challenges they faced with financial reporting requirements under the new model – and how those challenges have been addressed – so that they can properly prepare for the implementation. Networking and discussions to learn best practices can be valuable tools in understanding how to prepare for