Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Authors:
Tian-Shyug Lee;Chih-Chou Chiu;Yu-Chao Chou;Chi-Jie Lu
Affiliations:
Graduate Institute of Management, Fu-Jen Catholic University, Hsin-Chuang, Taipei, Taiwan;Institute of Commerce Automation and Management, National Taipei University of Technology, Taipei, Taiwan;Department of Business Administration, Fu-Jen Catholic University, Hsin-Chuang, Taipei, Taiwan;Department of Industrial Engineering and Management, Ching Yun University, Chungli, Taoyuan, Taiwan
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process, and interpretative difficulties have often been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.