Application research of consumer credit Score model based on SVM and DEA

Clustering and Classification are the most popular techniques in credit scoring. Most of the hybrid models are lack of revising abilities. To overcome this limitation, a two stages credit scoring model using SVM and DEA is proposed in this paper. Firstly constructing a SVM model and classifying all customs to two groups which are efficient and inefficient group, then performing DEA for those lower-efficiency customers and proposing the way to improve their efficiency. This study also performs an empirical research based on the credit card database of a bank. The results show that the SVM has great ability to predict the efficiency and the combined model can provide an indeed improvement for bank to improve the efficiency of non-efficient customer.