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http://dx.doi.org/10.7465/jkdi.2015.26.6.1417

Developing the high risk group predictive model for student direct loan default using data mining  

Choi, Jae-Seok (Statistics & Analysis Team, Korea Student Aid Foundation)
Han, Jun-Tae (Statistics & Analysis Team, Korea Student Aid Foundation)
Kim, Myeon-Jung (Statistics & Analysis Team, Korea Student Aid Foundation)
Jeong, Jina (Statistics & Analysis Team, Korea Student Aid Foundation)
Publication Information
Journal of the Korean Data and Information Science Society / v.26, no.6, 2015 , pp. 1417-1426 More about this Journal
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;
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Times Cited By KSCI : 4  (Citation Analysis)
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