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.
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References
Breiman L (1996) Bagging predictors. Mach Learn 24:123–140
Breiman L (2001) Random forests. Mach Learn 45:5–32
Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):1–26
Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121:256–285
Freund Y, Schapirer E (1997) A decision-theoretic generalization of online learning and an application to boosting. J Comput Syst Sci 55:119–139
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion). Ann Stat 28:337–407
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, New York
Ruan L (2010) An empirical study of feature extraction in the analysis of credit card risk. Technical report. Chongqing Three Gorges University
Schapire R, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651–1686
Schapirer E (1990) The strength of weak learn ability. Mach Learn 5:197–227
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Ling-ying, R. (2013). An Application of Classification Models in Credit Risk Analysis. In: Xu, B. (eds) 2012 International Conference on Information Technology and Management Science(ICITMS 2012) Proceedings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34910-2_45
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DOI: https://doi.org/10.1007/978-3-642-34910-2_45
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