Mining the customer credit using artificial neural networks and multivariate adaptive regression splines

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure in integrating artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the designed neural networks model. To demonstrate the effectiveness and feasibility of the proposed approach, credit scoring tasks are performed on one bank housing loan dataset. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an efficient alternative in handling credit scoring tasks.