A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

Authors:
Tian-Shyug Lee;I-Fei Chen
Affiliations:
Graduate Institute of Management, Fu-Jen Catholic University, No. 510, Chung-Cheng Road, Hsinchuang, Hsin-Chuang, Taipei 24205, Taiwan, ROC;Graduate Institute of Business Administration, Fu-Jen Catholic University, Hsin-Chuang, Taipei, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

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Abstract

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with 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 neural networks model. To demonstrate the effectiveness and feasibility of the proposed modeling procedure, credit scoring tasks are performed on one bank housing loan dataset using cross-validation approach. 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 alternative in handling credit scoring tasks.