THE APPLICATION OF SVM AND BNN IN CREDIT RISK ANALYSIS
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.
Credit rating analysis Support vector machines Backpropagation neural networks Input variable contribution analysis.
Xu Honge Li Yongchen Hu Yunlong Zhang Jie
Business Administration, North China Electric Power University, Baoding, China
国际会议
北京
英文
2007-11-01(万方平台首次上网日期,不代表论文的发表时间)