TITLE:
Study of Personal Credit Evaluation Method Based on PSO-RBF Neural Network Model
AUTHORS:
Shuai Li, Yuanmei Zhu, Chao Xu, Zongfang Zhou
KEYWORDS:
Personal Credit Evaluation; PSO Algorithm; RBF Network; PSO-RBF Model
JOURNAL NAME:
American Journal of Industrial and Business Management,
Vol.3 No.4,
July
26,
2013
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.