An Empirical Study on Credit Scoring Model for Credit Card by Using Data Mining Technology

This paper investigates the credit scoring accuracy of five data mining technologies for bank credit cards: C5.0 decision tree, neural network, chi-squared automatic interaction detector, stepwise logistic model and classification and regression tree. Firstly, we extract a comprehensive variable from the raw data by using principle component analysis to indicate the customers´ default or not. Then we build the credit scoring models using data mining technologies and compare forecasting effects of the five models. Finally, we discuss how to classify non-defaulting applicants by using stepwise logistic model extensively.