Browse > Article
http://dx.doi.org/10.7465/jkdi.2013.24.3.411

A study on the analysis of customer loan for the credit finance company using classification model  

Kim, Tae-Hyung (SAS Korea)
Kim, Yeong-Hwa (Department of Applied Statistics, Chung-Ang University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.3, 2013 , pp. 411-425 More about this Journal
Abstract
The importance and necessity of the credit loan are increasing over time. Also, it is a natural consequence that the increase of the risk for borrower increases the risk of non-performing loan. Thus, we need to predict accurately in order to prevent the loss of a credit loan company. Our final goal is to build reliable and accurate prediction model, so we proceed the following steps: At first, we can get an appropriate sample by using several resampling methods. Second, we can consider variety models and tools to fit our resampling data. Finally, in order to find the best model for our real data, various models were compared and assessed.
Keywords
Big data; data mining; decision tree; loan; oversampling; risk management;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Berry, M. and Linoff, G. (2011). Data mining techniques: For marketing, sales and customer relationship management, Wiley, New York.
2 Breiman, L. (1984). Algorithm CART, California Wadsworth International Group, Belmont, CA.
3 Cho, K. H. and Park, H. C. (2011a). A study on decision tree creation using intervening variable. Journal of the Korean Data & Information Science Society, 22, 671-678.   과학기술학회마을
4 Cho, K. H. and Park, H. C. (2011b). A study on removal of unnecessary input variables using multiple external association rule. Journal of the Korean Data & Information Science Society, 22, 877-884.   과학기술학회마을
5 Chung, H., Kang, C. and Kim, K. C. (2008). A study on the effect of oversampling for unbalanced data. Journal of the Korean Data Analysis Society, 10, 2089-2098.
6 Hartigan, J. A. (1975). Algorithm CHAID, John Wiley and Sons, New York.
7 Kang, H and Han, S. (1999). Data mining methodology and application, Free-Academy, Seoul.
8 Park, H. C. (2010). Association rule ranking function by decreased lift influence. Journal of the Korean Data & Information Science Society, 21, 397-405.   과학기술학회마을
9 Quinlan, J. R. (1993). C4.5: Programs for machine learning, San Mateo, Morgan Kaufmann, CA.
10 Berry, M. and Linoff, G. (1997). Data mining techniques: For marketing, sales and customer support, Wiley, New York.