The Financial Accounting Standards Board (FASB) announced in 2016 a new accounting standard introducing the Current Estimate of Credit Losses, or CECL, methodology for estimating allowances for credit losses. The new methodology for estimating expected credit losses is intended to simplify loss recognition by allowing one standard method for estimating future losses which includes a more consistent definition of when a future loss is 'probable'.

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Who needs to perform a CECL estimate? FASB is phasing in the CECL requirement, beginning with publicly traded companies.

Publicly traded companies – 2023, end of year filings with the SEC must include a CECL compliant loss estimate. Auditors will review methods for estimating losses to ensure compliance with the new CECL standards. See SEC rules and taxonomy here.

Mid-Sized businesses – no legal requirement exists as yet for mid-sized businesses to file CECL compliant loss estimate models. However, businesses with lines of credit may be subject to more stringent requirements for estimating impairments to trade receivables and other assets.

Small businesses – no CECL requirement is scheduled in the near term for independently financed businesses.

A CECL loss estimate is one final number. That number is comprised of a consolidation of loss estimates which are derived from the various business units or channels in which a business operates. Those loss estimates are generated by credit analysts who are tasked with producing defensible values which represent the losses that the business realistically expects to incur.

Under CECL guidelines, the recognition of an expected loss cannot be deferred until it is realized. It must now be classified as an expected loss, if the loss is'probable'.

Pre-CECL, treasury had broad discretion regarding what methodology it could use to recognize expected credit losses. As A/R is a large asset for many companies, it could be advantageous to delay the recognition of losses until they were highly probable, or actually incurred.

In practice, many credit analysts would estimate future A/R losses by taking the actual credit losses from the prior year, and then applying 'adjustment factors'. These factors might include the tightening of lending standards, changes in the economy, transitioning risky customers to cash, so forth. In relatively normal times (pre Covid) it was acceptable to take actual losses from a year such as 2018, and then apply a '+2%' or '-3%' to generate a loss estimate for 2019.

The enforcement of consistency in estimating losses is intended enable financial institutions to have better forward insights regarding when borrowers may take write downs, thus enabling all parties to be prepared with sufficient loan loss reserves.?

We'll walk you through the key steps involved in creating a simple CECL loss estimate model. Know before you get started that creating a CECL loss estimate generally requires a significant investment in the licensing or development of a loss estimate model. Under CECL guidelines, a loss estimate model must allow for the recognition of losses when they are deemed ‘probable’. The assumptions that the model are based upon must be mathematically defensible.

The development of a CECL loss estimate first requires the A/R portfolio be organized into groupings of 'statistically similar' businesses. The most sensible way to do this is to organize borrowers by: industry sector, company size, and then geographic location. (Note- avoid trying to organize constituent borrowers by credit score or expected loss rate as you will end up with very wide variances in types of borrowers.)

Once you have you’re A/R portfolio organized, then you'll need to apportion your total A/R in dollar amounts to each of the borrower groups, for example: if you have a $10 million A/R portfolio, and 3% of that portfolio is from mid-sized businesses in the manufacturing sector, you’ll enter $300,000 in that cell of your model.

Then you should find the data that represents estimated future losses for each borrower group. These data can be a single value (e.g.: 7%) or a range of values (e.g.: 5% to 9%). These data can be a challenge to locate as most credit firms provide historical outcomes, rather than forward looking estimates.

We offer a program that creates the foundation of a CECL compliant loss estimate model. Our Commercial Credit Modeler (CCM) enables you define the industry sectors and geographic regions of the U. S., and pull forward looking default probability data relating to your query. The results are provided instantly in an Excel sheet. Within the sheet, default probability values are provided in a format which enables credit policy planning, and risk management decisioning. Within a CCM file, all data are organized in a structure that is designed to enable you to move towards creating a true, CECL compliant loss estimate model.

Irrespective of any regulation, it's a good business decision to have a reliable loss estimate model. Whether you call it 'CECL light' or just a embrace a newer and more practical means of constructing a model that enables you to predict and manage risk, having a reliable loss estimate model is smart business. Especially in the current environment of challenging credit conditions.

In addition to the benefits that the CCM brings to the treasury and credit departments, the CCM can assist in overall business planning. Possibly there are industry sectors you might reconsider after viewing the associated risk. Sales and Marketing should be aware of where risks may be unacceptable to credit, and where opportunities may yield better future returns. The CCM can add value across multiple departments in any organization.