Credit scoring has become a critical and challenging management science issue as the credit industry has been facing stiffer competition in recent years. Many classification methods have been suggested to tackle this problem in the literature. In this paper, we investigate the performance of various credit scoring models and the corresponding credit risk cost for three real-life credit scoring data sets. Besides the well-known classification algorithms (e.g. linear discriminant analysis, logistic regression, neural networks and k-nearest neighbor), we also investigate the suitability and performance of some recently proposed, advanced data mining techniques such as support vector machines (SVMs), classification and regression tree (CART), and multivariate adaptive regression splines (MARS). The performance is assessed by using the classification accuracy and cost of credit scoring errors. The experiment results show that SVM, MARS, logistic regression and neural networks yield a very good performance. However, CART and MARS's explanatory capability outperforms the other methods.

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