Credit risk assessment in commercial banks based on multi-layer SVM classifier

Authors:
Wei Sun;Chenguang Yang
Affiliations:
Department of Economy Management, North China Electric Power University, Hebei, China;Department of Economy Management, North China Electric Power University, Hebei, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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Abstract

According to the analysis of credit risk assessment in commercial banks, a set of index system is established. The index system combines financial factors with non-financial factors for credit risk assessment. The credit rating is separated into five classes- normality, attention, sublevel, doubt and loss. To classify the credit risks of five classes, a multi-layer support vector machines (SVM) classifier is established to assess the credit risk. In order to verify the effectiveness of the method, a real case is given and BP neural network is also used to assess the same data. The experiment results show that multi-layer SVM classifier is effective in credit risk assessment and achieves better performance than BP neural network.