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JANUARY 2001 Ė ISSUE 01-01

Editor:  Mary  S  Schaeffer


The story of how one skeptical credit manager developed a credit-scoring model for his company is one all credit pros can learn from. Ron Wells, a credit manager for an oil company in England and the first non-U.S. credit professional to earn the coveted certified credit executive (CCE) designation from NACM, explained how he built the model at the recent FCIB Global Conference in New York. He began by discussing his goals and then gave a detailed breakdown of exactly what he put into the model.


Wells says that his employer wanted to make credit limit decisions in a standardized, systematic, and factual way. Its goal was to make credit decisions that both align with and support the companyís overall strategy. It wanted to speed up credit decisions using technology as much as possible. He says that the result of this strategy would be to replace the credit analyst. The credit staff would still be needed, but some of its functions would be eliminated. Wells was a believer that credit analysis is still more of an art than a science, so to say that he was a bit dubious would be putting it kindly.

Credit decisions, he says, are typically based on old information, if available, a different set of facts and feelings in each case, and an intuitive weighting system based on personal experience. Those practiced credit professionals reading this will know that what he is saying is true. They also realize that most of the time, the traditional way produces a fairly good result. But, the question remains: Is there a better way? He puts forth the following hypothesis: Is it possible to distill all these factors into a manageable generic list of questions, which require factual answers that can be weighted and thus produce consistent credit decisions? The answer to this question, he was to discover, is yes.

Building the Model

The accompanying PowerPoint shows the input for the scoring model Wells developed. He says that it is a work in progress because he continues to fine-tune it. If, at first glance, some of the questions seem a bit odd, Wells has a reason for including them. He says that some questions are proxies for other information.

He uses eCredit.comís CCEx Expert Comments Synthetic Summary Score on the financial information.

If Expert Comments judges the customerís financial strength to be:

excellent he scores = 100;

good he scores = 80;

above average he scores = 60;

average he scores = 40;

marginal he scores = 20; and

poor he scores = 0.

The scores from the model are then weighted using a simple algebraic formula. Wells did not share his weights, which may change as a companyís appetite for risk increases or decreases. The output from the model is a trade credit score. The credit score is then used in a simple algebraic equation to set a credit limit for the customer. The limit is based on the score and the customerís equity. The better the score, the larger the percent of equity that will be used to determine the allowable credit limit.

Wells incorporates the following rules in setting limits:

the limit shall not exceed 10% of equity amount of his company;
the limit must not exceed the requested amount, bearing in mind the customerís normal purchasing pattern;
and the limit shall not exceed the customerís assessed ability to pay trade-related obligations in the normal course of business, within normal terms. This amount is determined using the credit score and the customerís equity.

For example, a customer with the very highest trade credit rating might be given a line equal to 25% of its shareholder equity, while one with the lowest rating might only be allowed 4% of its shareholder equity. The lowest rating in this case refers to the lowest rating that a company might still be willing to sell on open-account terms.

Before and After

With the system up and running, Wells has a chance to reflect on the benefits of the process. For starters, it should be said that he appears to have changed his mind and can now see the benefits of credit scoring. Before, he says, each analyst made individual decisions. Now the system drives the decisions, and analysts feed the system. The result of the individual decision-making was inconsistent decisions and inconsistent review of available information. With the system, consistent decisions are made based on a mandatory list of factors.

One of the difficulties with the old approach is that adjusting a firmís appetite for risk is difficult and, in addition, there is no basis to analyze portfolio risk. With a credit-scoring model, risk appetite can be adjusted by amending the weights or limits in the model. Portfolio risk is apparent, providing risk managers with the information they need to manage that risk.

Using This Information

The model as developed by Wells will not work in every organization. Different companies in different industries with varying appetites for customer payment risk will assess the various factors accordingly. Some will not need to include all the elements used by Wells. For example, a company selling exclusively in the domestic market would not need the country risk component. Others may want to add factors not included by Wells. However, his work serves as an excellent foundation for those interested in building their own models. Studying his methods and approach will also provide credit professionals with an understanding of credit scoring in the B2B world.

Reprinted with permission.

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Last Updated:  February 01, 2020 18:38 -0000