Credit Risk Evaluation: a Review and the Application using Backpropagation Neural Networks

摘要：

The dramatic increase in the corporate failures over the last decade has led to the increasing interests in failure prediction model. This paper reviews various quantitative methods and adopts one neural network approach,Backpropagation Neural Networks (BPNN),to identify the credit risk. BPNN gives convincing 54.55％ bankruptcy and 100％ non-bankruptcy out-of-sample prediction accuracies. The promising results validate that the neural network approach is an excellent supplement to the traditional prediction techniques and it provides management tremendous benefits on credit approval,loan securitization and loan portfolio management.

关键词： Backpropagation Neural Networks (BPNN) Credit Risk Bankruptcy

作者: Zijiang Yang Desheng Wu Guangyu Fu

作者单位: School of Information Technology York University,4700 Keele Street,Toronto,Ontario,Canada,M3J 1P3 Rotman School of Management University of Toronto,105 St. George St.,Toronto,Ontario,Canada,M5S 3E6 161 Bay Street,11th Floor,Treasury and Risk Management,CIBC,Toronto,Ontario,Canada MSJ 2S8

会议类型: 国际会议

会议名称: The First International Conference on Management Innovation(ICMI 2007)(管理创新会议)

会议地点: 上海

会议语种:英文

页码: 361-365

在线出版日期: 2007-06-04（万方平台首次上网日期，不代表论文的发表时间）