Mining the customer credit by using the neural network model with classification and regression tree approach

In this study, a combination of classification and regression tree (CART) and neural network techniques is proposed to determine whether the predictive capability can be enhanced in credit scoring model. To demonstrate the effectiveness of the proposed approach, these techniques are applied to data from a large bank in Taiwan. In the approaches of neural network and the combined model, the backpropagation learning technique with various learning rates is extensively studied to determine the connection weights between neurons. The number of hidden neurons is also varied to see its effect on the converge rate. Our results indicate that the proposed combined approach predicts much accurately and converges much faster than that the conventional CART method or the neural network approach.