An Application of Classification Models in Credit Risk Analysis
A default risk is defined as the possibility, that a borrower will not be able to pay back the principle or interest associated with a lending. Credit card business has high risk of delinquency as there is no collateral required before borrowing the money. Lenders usually collect a lot of information to learn the consumer risks. A conventional method to this problem is to examine combinations of the information variables that art likely to have influence. However, hunch can leave out important variables without being noticed. In this article, we introduce statistical models to conveniently predict the default risk based on an application to a real data of credit card business. Several potential improvements are also discussed.
Credit Risk Classification Models Logistic Regression Boosting Random Forests
Ruan Lingying
School of Applied Technology, Chongqing Three Gorges University,Wanzhou, Chongqing,China
国际会议
重庆
英文
1692-1695
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）