Publisher : Korean Data and Information Science Society
DOI : 10.7465/jkdi.2015.26.6.1417
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;
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.
Data mining;decision tree;student direct loan;
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