THE APPLICATION OF SVM AND BNN IN CREDIT RISK ANALYSIS

摘要：

In this paper we have a study of credit risk analysis from the credit-rating angle and attempt to extend previous research in two directions. Firstly, we apply a relatively new learning algorithm, support vector machines (SVM), to the credit-rating prediction problem and expect to improve prediction accuracy by adopting this new algorithm. Secondly, we apply the results from previous research on neural network model interpretation to the credit-rating problem, and try to provide some insights about the credit-rating process through neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and China markets.

关键词： Credit rating analysis Support vector machines Backpropagation neural networks Input variable contribution analysis.

作者: Xu Honge Li Yongchen Hu Yunlong Zhang Jie

作者单位: Business Administration, North China Electric Power University, Baoding, China

会议类型: 国际会议

会议名称: 2007年技术创新、风险管理暨供应链管理国际研讨会

会议地点: 北京

会议语种:英文

在线出版日期: 2007-11-01（万方平台首次上网日期，不代表论文的发表时间）