An Application of Classification Models in Credit Risk Analysis

Ruan Ling-ying ()
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Ruan Ling-ying: Chongqing Three Gorges University

A chapter in 2012 International Conference on Information Technology and Management Science(ICITMS 2012) Proceedings, 2013, pp 399-404 from Springer

Abstract: Abstract 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 are 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.

Keywords: Credit risk; Classification models; Logistic regression; Boosting; Random forests (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-34910-2_45

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DOI: 10.1007/978-3-642-34910-2_45

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