Classifying Customers as High or Low Credit Risks

Classification table with independent variables in the rows and classification scores for the two categories of Previously defaulted (No and Yes) in the columns — Figure 1. Classification functions

The classification functions are used to assign cases to groups. There is a separate function for each group. For each case, a classification score is computed for each function. The discriminant model assigns the case to the group whose classification function obtained the highest score.

The coefficients for Years with current employer and Years at current address are smaller for the Yes classification function, which means that customers who have lived at the same address and worked at the same company for many years are less likely to default. Similarly, customers with greater debt are more likely to default.

Data for cases 701 and 703 displayed in Data view in the Data Editor — Figure 2. Bankloan.sav data for cases 701 and 703

For example, consider cases 701 and 703. Case 701 has had the same employer for 16 years, lived at her current address for 13 years, and has debt equal to 10.9% of her income, $540 of which is credit card debt.

Predicted probabilities for cases 701 and 701 displayed in Data view in the Data Editor — Figure 3. Predicted probabilities of default for cases 701 and 703

The discriminant model predicts that there is only about an 8% chance that she will default on the loan, so she is a good credit risk. Case 703 has had the same employer and lived at the same address for fewer years and has greater debts, so the model sees him as a poor credit risk.