Study of Personal Credit Evaluation Method Based on PSO-RBF Neural Network Model

Shuai Li, Yuanmei Zhu, Chao Xu, Zongfang Zhou
DOI: 10.4236/ajibm.2013.34049                

Abstract

Personal credit evaluation is the basic method for the commercial banks to avoid the consumer credit risk. On one hand, the credit behavior of individuals is complex; on the other hand the personal credit assessment system in our country is not sound, assessment methods are mostly objective, therefore, more and better scientific methods for credit risk assessment need to be introduced. This paper proposed a method for personal credit evaluation based on PSO-RBF neural network, which used PSO algorithm to optimize the parameters of RBF neural network, then applied the optimized RBF neural network in the personal credit evaluation. This method combined the global searching ability of PSO algorithm and the high effectiveness of local optimize of RBF together, overcame the unstabitily of PSO algorithm and the drawback of RBF which easily leads to local minimum. The result shows that the personal credit assessment method based on PSO-RBF neural network is highly accurate in classification and prediction, and is suitable in personal credit assessment and prediction.

Keywords

Personal Credit Evaluation; PSO Algorithm; RBF Network; PSO-RBF Model

Conflicts of Interest

The authors declare no conflicts of interest.

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