Statistical Tools for Managing Accounts

Data scoring models can help predict your customer's ability to pay

With the current economic environment and a surge in defaults and bankruptcies, there has been greater emphasis on being able to ascertain the risks of customers and statistical tools to measure your customer’s ability to pay. On a past CMA webinar, “Statistical Tools for Managing Accounts,” Prof. Jack Williams, JD, CIRA, CDBV of Georgia State University praised the value of using various types of statistical models to aid credit executives in their decision-making process. The presentation also served as a primer for the liquidity, debt and performance ratios, the tools used to weigh the credit risk of customers.

“Use of statistics can be a valuable tool,” said Williams. “Statistical techniques are a way you can allow the data to tell you a story about your customer. Statistics unlock the doors to all sorts of information and can be used to compare a customer to the rest of that customer’s industry.”

He added, “Any statistical analysis properly framed will answer fundamental questions in a credit department’s operations.”

Modeling and scoring techniques have been hailed as being objective in assessing the risks of a customer, helping set opening lines of credit, as well as establishing a baseline for that customer’s historical payment cycle. It’s a lifecycle tool. There are a number of commercial options available and CMA offers such services as well.

“The interesting thing about statistical tools is that they allow you to look at a lot of customers simultaneously,” said Williams. “If you have a shop like the ones I grew up in: you’re underfunded, undermanned and overworked. So, any type of tool that makes you more efficient, particularly if it allows you to manage a lot of customers, a lot of invoices, is going to be a better tool at the end of the day.”

Williams noted that people are often turned off by statistical tools and modeling because it is routinely seen as being math intensive. The reality is, though there is math involved, it is not something only accomplished by rocket scientists.

“Statistics is less about numbers and more about patterns,” said Williams. “And that’s what we do intuitively as good credit managers. In fact, you’re already doing statistical analysis.” And by turning to an unemotional computing technique, Williams said credit managers can remove subjectivity from the process and embrace a tool that is reliable and consistent. Though he added, “They do not replace personnel and they certainly do not replace discretion. They augment credit discretion.”

Williams’ detailed discussion of statistical modeling covered everything from aging receivables, to analyzing raw data, to various types of averages, and even to dreaded terms like standard deviation. But he continued to tout the
importance and efficiency of modeling as well as the ease of use. Even basic office tools like Microsoft Excel can allow credit managers to start doing their own statistical modeling. Most have access and are familiar with the software.

“Excel gives you access to very amazing, powerful statistical techniques that are embedded in the Data Analysis Toolpak,” said Williams, going into detail into how simple it was to load from the Tools menu. “It’s very, very easy to use. Among other things, it’s going to give you summary statistics. It’s going to give you the averages so you can get an indication of what your particular payment cycle looks like with a particular customer or customers within a particular product or service line that you provide. It’s also going to measure variability.”

For more information about the tools that CMA-partner credit bureaus offer on credit scoring and modeling, contact CMA at 818-972-5300.

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