On Credit Scoring Estimation: Overview of Quantitative Methods for Credit Scoring Illustrated with a Case Study

Credit scoring methods became a standard tool of banks and other financial institutions, direct marketing retailers and advertising companies to estimate whether an applicant for credit/goods will pay back his liabilities. In this book we give a short overview of credit scoring and its quantitative methods. We investigate the usage of some of these methods and their performance on a real data set taken from a French bank. Our results indicate that the methods used, namely the logistic regression, multi-layer perceptron (MLP) and radial basis function (RBF) neural networks give very similar results, however, the traditional logit model seems to outperform the other techniques. We also describe RBF architecture and a simple RBF program that we implemented in the statistical computing environment XploRe.