Traditionally, corporate trade credit limits have been set based on customer size, an internal or external credit score, and a qualitative sense of risk appetite. These limits have been effective in minimizing write-offs, principally because they are conservative.
If robust, more precise, probabilities of default can be obtained, those credit limits can be adjusted to yield higher margins and extend credit where economically justified. The result can drive higher volumes to some existing customers and new sales to customers previously denied credit.
To do so, the credit limit setting process must focus on net value added, rather than on loss minimization. One side benefit of the approach is the ability to credit-adjust the pricing of a given transaction. This way, the cost of credit is explicit and can be added to the required margins.
Forgone margins and growth
More than $500 billion dollars in unsecured trade credit are currently extended in the United States by wholesale sellers, marketers, or traders. The average is roughly $200 million in Accounts Receivables (AR) per firm.1 Many of the mid-size to larger marketing entities hold AR books larger than regional banks. Yet they manage these AR positions with the primary goal of limiting write-offs.
Inevitably, firms turn away profitable customers, and sales to non-investment grade counterparties are highly restricted. The seller’s return on capital becomes limited, and total sales and profits suffer, all needlessly. Rethinking how credit limit tables are constructed can alleviate these restrictions for many customers, directly increasing both sales margins and revenues.
Traditional risk management of trade receivables
Many wholesale marketer/traders have traditionally measured and monitored credit risk by assigning credit limits to counterparties. They have also tracked limits and usage for individual customers and the portfolio as a whole. Credit limit assignments are made based on pre-approved tables. These tables show that a maximum suggested limit for customers of a given size (sales or net worth) and creditworthiness (internal or external scoring). These tables typically suggest higher limits for larger customers and lower limits for heightened credit risk customers, sometimes allocating zero unsecured credit to non-investment grade counterparties.
This approach abstracts from other potentially useful information, such as customer margins or target returns, achieve high levels of simplicity and usability. Employing such a table lets the firm focus on its primary business model and still maintain a low level of write-offs by setting credit limits conservatively. This strategy is doubly important when internal scoring methods are inconsistent, when there is a lack timely customer information, or when external credit scores are stale. With a history of low defaults or write-offs, sellers become comfortable with a conservative schedule of maximum limits. They can also see how it effectively limits their overall credit risk exposure.
An economic approach to limit setting
Credit risk aversion and conservatism are understandable for firms in lines of business other than banking. However, it can be worth evaluating just how much conservatism is inherent in the standard corporate approach to credit limits. The benchmark to assess is the “risk-neutral” economic maximum credit limit, or that limit calculated with an extra risk premium to account for credit risk aversion. The maximum economic credit limits (MECL) are reached when higher limits would not result in any further expected profit (see the Appendix). The intuition behind the economic limits is straightforward. The margin on the trade flow must cover the expected loss due to non-payment. Also, it must cover the cost of capital for being able to withstand that potential loss.
The overall implications of a traditional credit limit table are best seen in a side-by-side comparison of MECL results (Table 1). Here, we use an actual credit limit table used by an industrial wholesaler as an example of a typical structure. The next two columns show the maximum economic credit limits for variously scored customers. We first assume a 3.5% profit margin on sales, a 7% cost of capital, and a 10% excess return on capital as a credit risk aversion premium. The sales level and AR level were chosen so that the Aaa-rated customer limits match those in the industry example.
The last column in Table 1 shows the MECL results, holding all assumptions in the 3.5% margin calculations constant, but increasing the sales margin to 5.5%.
The calculated MECL columns show higher limits for better credit risk customers. That unsecured credit may be denied to some high credit-risk customers, just as we see in the Industry Example limits. But there are also three important differences in the MECL results:
» Limit reduction with credit deterioration. Simplified credit limit structures, such as the Industry Example, reduce the maximum credit limit by 70% for a rating reduction of Aaa to Ba. The MECL approach reduces the maximum limit by less than 5%. The MECL reduction is small, because the default probability of the Aaa-rated customer and the Ba-rated customer are both low (0.002% and 0.9%, respectively) and similar.
» Limit restriction with credit deterioration. The Industry Example limits deny any unsecured credit to potential customers rated B or below. The B credit rating category has a historical default rate of about 3.4% per year. This rate creates the distinct likelihood that some credit losses might be experienced from these buyers. Yet the MECL results show that even a modest margin of 3.5% is enough to make transactions with this customer class profitable.
» Margin effects. The impact of higher customer profitability clearly increases the maximum economic credit limits and makes extending credit to higher risk customers much more viable. The additional profits offset expected losses and capital charges, and can result in significant revenue and profit growth.
