As discussed in
LI 7.3.6 (after adoption of
ASU 2022-02) or
LI 7.3.6A (before adoption of
ASU 2022-02), once reporting entities adopt the expected credit loss model, determining what data is relevant in estimating expected credit losses will become a critical part of the allowance assessment. Under the CECL model, reporting entities can leverage historical loss data, but CECL also requires forward looking information and forecasts to be considered in determining credit loss estimates.
Most reporting entities have access to historical loss data that they have been using to estimate an allowance for doubtful accounts under the incurred loss model. This data allows reporting entities to estimate the percentage of uncollectible accounts or the amount of bad debt expense, typically as a percentage of accounts receivable, sales, or a combination of these metrics. Reporting entities may aggregate this data and analyze how it trends over time. Reporting entities can utilize historical data to understand and identify factors that resulted in historical credit losses and incorporate those factors into their analysis of future expected credit losses.
Reporting entities may use historical loss data, adjusted for current conditions and reasonable and supportable forecasts in conjunction with an accounts receivable aging matrix, to form a view of the relative size of credit losses to be expected under the CECL impairment model. For example, data may indicate that as a customer moves from the 60- to 90-day delinquency category to the 90- to 120-day delinquency category, the expected credit losses increase. A reporting entity may use this analysis to identify customers on which it will perform further credit analysis, such as customers who have particularly large uncollectable accounts or who have receivables that have been aged for a long period of time. Reporting entities may have also performed an analysis to determine whether there were significant changes in the credit ratings of their customers, as decreases in the credit ratings of customers may indicate a deterioration in credit quality. This analysis will be important in the CECL model, as the results of the analysis may lead a reporting entity to increase its expectation of credit losses.
Understanding the relationship between the reporting entity, the industry, and the customer base is an important starting point in assessing which factors may impact the assessment of expected credit losses. Understanding customer demographics, payment terms offered in the normal course of business to customers, and industry-specific factors that could impact the reporting entity’s receivables is critical to forming the basis of the expected credit loss analysis.
In addition, under an expected loss model, reporting entities are required to consider available external data in their analyses. These external data points include macroeconomic factors, such as economic growth trends. Companies will need to assess the degree of correlation between these data points and the reporting entity’s loss experience and loss forecasts to determine the impact macro (and micro) economic factors have on loss experience. Judgment will be required to determine how historical loss information, as well as the macroeconomic factors that were present when the historical losses took place (as compared to those that may exist today and in the future), should be incorporated into current period credit loss estimates.
Question LI 7-24
Should a reporting entity consider factors unrelated to credit that could impact the expected cash flows of a receivable (e.g., product returns, cash discounts, volume rebates, discounts for early payment) when calculating its allowance for credit losses?
PwC response
No. When developing its allowance for credit losses, a reporting entity should ensure that factors unrelated to credit that may impact expectations of cash flows are excluded. Items that impact the amount of cash to be received that are unrelated to expected credit losses should be accounted for using other GAAP (e.g., revenue guidance).