JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Developing the high risk group predictive model for student direct loan default using data mining
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Developing the high risk group predictive model for student direct loan default using data mining
Choi, Jae-Seok; Han, Jun-Tae; Kim, Myeon-Jung; Jeong, Jina;
  PDF(new window)
 Abstract
We develop the high risk group predictive model for loan default by utilizing the direct loan data from 2012 to 2014 of the Korea Student Aid Foundation. We perform the decision tree analysis using the data mining methodology and use SAS Enterprise Miner 13.2. As a result of this model, subject types were classified into 25 types. This study shows that the major influencing factors for the loan default are household income, national grant, age, overdue record, level of schooling, field of study, monthly repayment. The high risk group predictive model in this study will be the basis for segmented management service for preventing loan default.
 Keywords
Data mining;decision tree;student direct loan;
 Language
Korean
 Cited by
 References
1.
Barney, D. K., Graves, O. P. and Johnson, J. D. (1999). The Farmers Home Administration and farm debt failure prediction. Journal of Accounting and Public Policy, 18, 99-139. crossref(new window)

2.
Feldman, D. and Gross, S. (2005). Mortgage default: Classification tree analysis. Journal of Real Estate Finance and Economics, 30, 369-396. crossref(new window)

3.
Hong, C. S. and Bang, G. (2008). Modified Kolmogorov-Smirnov statistic for credit evaluation. The Korean Journal of Applied Statistics, 21, 1065-1075. crossref(new window)

4.
Jin, S. K., Kim, K. R. and Park, C. (2012). Cutpoint selection via penalization in credit scoring. The Korean Journal of Applied Statistics, 25, 261-267. crossref(new window)

5.
Jung, J. H. and Min, D. K. (2013). The study of foreign exchange trading revenue model using decision tree and gradient boosting. Journal of the Korean Data & Information Science Society, 24, 161-170. crossref(new window)

6.
Kim, A. and Kim, J. S. (2006). Classification of the demand groups for the rural college student loans by decision tree method. Korean Journal of Sociology of Education, 16, 51-75.

7.
Kim, T. H. and Kim, Y. H. (2013). A study on the analysis of customer loan for the credit finance company using classification model. Journal of the Korean Data & Information Science Society, 24, 411-425. crossref(new window)

8.
Thomas, L. C. (2000). A survey of credit and behavioral scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16, 149-172. crossref(new window)

9.
Yang, B., Li, L. X., Ji, H. and Xu, J. (2001). An early warning system for loan risk assessment using artificial neural networks. Knowledge-Based System, 14, 303-306. crossref(new window)

10.
Zurada, J. and Zurada, M. (2002). How secure are good loans: Validating loan-granting decisions and predicting default rates on consumer loans. The Review of Business Information Systems, 6, 65-83.