Combined Model of Empirical Study for Credit Risk Management

摘要：

In this paper,we studied the two most commonly used artificial intelligence methods (Multilayer Perceptron and Radial Basis Function network) to build the credit scoring model of applications,and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex.On this basis,the author combines econometric analysis of the experience,through logistic regression the model can filter the variables with a high degree of correlation,which greatly reduces the complexity of the model,while the model has a better explanation,and thus improve the effect of neural network prediction models.The method can also be used for a variety of artificial intelligence applications to improve forecast model results.

关键词： Multilayer Perceptron Radial Basis Function Neural Networks Logistic Regression Credit Risk

作者: Han Lu Han Liyan Zhao Hongwei

作者单位: School of Economics and Management Beijing University of Acronautics and Astronautics Beijing 100191 Dept.of Quality Assurance Rainier Technology Co., Ltd.Beijing 100085,China

会议类型: 国际会议

会议名称: 2010 2nd IEEE International Conference on Information and Financial Engineering(2010年第二届IEEE信息与金融工程国际会议 ICIFE 2010)

会议地点: 重庆

会议语种:英文

页码: 189-192

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）