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Credit Risk Assessment in Commercial Banks Based on Multi-layer SVM Classifier

  • Wei Sun
  • Chenguang Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

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.

Keywords

Support Vector Machine Credit Risk Commercial Bank Support Vector Machine Classifier Index System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Sun
    • 1
  • Chenguang Yang
    • 1
  1. 1.Department of Economy Management, North China Electric, Power University, Baoding 071003, HebeiChina

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