Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis, and Feedforward Networks

Abstract

Three different discriminant techniques are applied and compared to analyze a complex data set of credit risks. A large sample is split into a training, a validation, and a test sample. The dependent variable is whether a loan is paid back without problems or not. Predictor variables are sex, job duration, age, car ownership, telephone ownership, and marital status. The statistical techniques are logistic discriminant analysis with a simple mean effects model, classification tree analysis, and a feedforward network with one hidden layer consisting of three units. It turns out, that in the given test sample, the predictive power is about equal for all techniques with the logistic discrimination as the best technique. However, the feedforward network produces different classification rules from the logistic discrimination and the classification tree analysis. Therefore, an additional coupling procedure for forecasts is applied to produce a combined forecast. However, this forecast turns out to be slightly worse than the logit model.