Mining the customer credit using hybrid support vector machine technique

Credit scoring has become a critical and challenging management science issue, as the credit industry has been facing fiercer competition in recent years. Many methods have been suggested to tackle this problem in the literature. In this paper, we proposed hybrid support vector machine technique based on three strategies: (1) using CART to select input features, (2) using MARS to select input features, (3) using grid search to optimize model parameters. In order to verify the feasibility and effectiveness of the proposed hybrid SVM model, one credit card dataset provided by a local bank in China is used in this study. Analytic results demonstrate that the hybrid SVM technique not only has the best classification rate, but also has the lowest Type II error in comparison with CART, MARS and SVM and justify the presumptions that SVM having better capability of capturing nonlinear relationship among variables.