Study of Personal Credit Evaluation Method Based on PSO-RBF Neural Network Model
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- 4 School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China
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 effective ness of local optimize of RBF together, overcame the unstabitily of PSO algorithm and the drawback of RBF which eas ily 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.
- J. Z. Dai, “Exploration on the building of Personal Credit Risk System,” Financial Practice, Vol. 15, No. 3, 2002, p. 11.
- Z. F. Zhou, et al., “The Study of the Evolution Mechanism and Evaluation Methods of Emerging Technology Corporate Credit Risk,” Science Press, Beijing, 2010.
- W. E. Henley, “Statistical Aspects of Credit Scoring,” The Open University, Milton Keynes, 1995.
- J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” Proceedings of International Conference on Neural Networks, Orlando, 1997, pp. 1942-1948.
- P. Z. Fu and Y. X. Ying, “The Study of Neural Network Based on PSO-RBF and Its Application in Credit,” Chinese Journal of Scientific Instrument, Vol. 8, No. 1 2007, pp. 464-466.
- D. S. Wang, S. H. Li and X. P. Zhou, “The Method and Application of Raw Water Quality Evaluation Based on PSO-RBF Neural Network Model,” Journal of Southeast University (Natural Science Edition), Vol. 41, No. 5, 2011, pp. 1019-1023.
- M. F. Feng and J. C. Lu, “Use Particle Swarm to Optimize RBF Neural Network in Short-Term Traffic Flow Prediction,” Computer Simulation, Vol. 27, No. 12, 2010, pp. 323-326.
- B. X. Xiao, X. W. Wang and Y. F. Liu, “Simulation Research of Boiler Superheat System Identification Using MPSO-RBF Optimization Strategy,” Journal Of System Simulation, Vol. 19, No. 6, 2007, pp. 1382-1386.
- L. L. Li, “RBF Neural Network Integration Research and the Application in the personal Credit Assessment,” Shandong Normal University, Jinan, 2008.
- S. Yin and M. H. Jiang, “Personal Credit Assessment Combination Forecast Model Based on PSO,” Economic Research Guide, Vol. 3, No. 14, 2208, pp. 83-86.
- M. H. Jiang and X. C. Yuan, “The Construction and Application of PSO-SVM Model in Personal Credit Assessment,” Journal of Management, Vol. 5, No. 4, 2008, pp. 511-516.
- L. Y. Yu, “Evaluation Prediction Model Research on Commercial Bank Credit Risk,” Journal of Management, Vol. 6, No. 5, 2003, pp. 46-52.
- M. H. Jiang, “Research on Combination Forecast Method for Commercial Bank Personal Credit,” Harbin Institute of Technology, Harbin, 2006.
- H. Y. Tang and Z. G. Xu, “Neural Network Based on PSO-RBF and Its Application. Metallurgical Automation,” 2006, pp. 464-466.
- J. Wang, “The Establishment of Personal Credit Evaluation Index in Consumer Credit,” Rural Finance Ressarch, Vol. 21, No. 1, 2001, pp. 48-50.