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Reduce Credit Risk with Accurate Scoring Models

As the worldwide financial crisis deepens, one long-term impact is becoming clear: Technology is becoming an even more important component in how credit professionals perform their jobs, says Lyle Wallis, vice president of research at the Credit Research Foundation.

“You see the growth in technology everywhere—in the way companies are delivering invoices, automating their payment processes, and launching cash applications,” he explains.

One area drawing particular attention from savvy credit professionals today is the ability to better analyze the credit worthiness and risk of customers.

Financial management applications, such as those from Oracle, provide tools for developing credit scores, performing risk analysis of individual and groups of customers, and establishing policies for risk-based collections. These programs can help creditors segregate customers that may be good credit risks from those in jeopardy of going out of business in the months ahead.

For example, one account may be a large, national retail chain that is taking advantage of creditors by stretching its payments more than 30 or 45 days past due. Another account may be a small wholesaler that’s been paying its bills on time, but is near the end of its working capital because of steep declines in its revenues. Which is the better credit risk?

“It would be logical to chase the large retailer that is 90 days past due, rather than the company whose payments are current,” Wallis points out. “But in reality the strategy should be the other way around.” Accurate risk-assessment models can help creditors determine that the retailer will retain the capacity to pay eventually, while the smaller customer is in jeopardy of going out of business in the next 60 days, he explains.

Similarly, credit professionals can use financial models to identify solid long-term customers worthy of a continuing line of credit even if their short-term payment history has faltered.

Manually drawing distinctions like these from a customer base that may number in the tens of thousands can be impossible. “The only way to do this successfully is with automated processes that rely on statistical models and credit scoring,” Wallis says.

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