Creating an accurate bad debt forecast can be similar to reading tea leaves or consulting a crystal ball—it is very difficult to make actual results come anywhere near the forecast. The usual approaches are to either create a forecast based on specific expected losses or to assign a loss probability based on the age of various receivables. Neither approach works especially well.
An alternative with a greater level of accuracy involves assigning a risk class to each customer, and then assigning a loss probability to open receivables based on the risk class. Risk classifications can be calculated with elaborate in-house risk scoring systems, but there are many commercially-available alternatives available, such as FICO (Fair, Isaac and Company) scores for individuals or the Dun & Bradstreet Paydex and Financial Stress scores for businesses.
Here are the steps needed to create a bad debt forecast based on risk scoring:
Periodically obtain new risk scores for all current customers, excluding those with minimal sales.
Load the scores for each customer into an open field in the customer master file.
Print a custom report that sorts current customers in declining order by risk score.
Divide the sorted list into fourths (low risk through high risk), and determine the bad debt percentage for the previous year for each category.
Use the format in the following example to derive the bad debt percentage: