Mining the Customer Credit Using Classification and Regression Tree and Multivariate Adaptive Regression Splines

摘要

Credit scoring has become a very important task as the credit industry has been experiencing tough competition during the past few years. 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, and neural networks approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.