Classification and dimension reduction in bank credit scoring system

Abstract

Customer credit is an important concept in the banking industry, which reflects a customer's non-monetary value. Using credit scoring methods, customers can be assigned to different credit levels. Many classification tools, such as Support Vector Machines (SVMs), Decision Trees, Genetic Algorithms can deal with high-dimensional data. However, from the point of view of a customer manager, the classification results from the above tools are often too complex and difficult to comprehend. As a result, it is necessary to perform dimension reduction on the original customer data. In this paper, a SVM model is employed as the classifier and a "Clustering + LDA" method is proposed to perform dimension reduction. Comparison with some widely used techniques is also made, which shows that our method works reasonably well. © 2008 Springer-Verlag Berlin Heidelberg.