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Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt

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  • Jun†Tae Han
  • Jae†Seok Choi
  • Myeon†Jung Kim
  • Jina Jeong

Abstract

Using direct loan data for 2012 to 2014 from the Korea Student Aid Foundation, we develop a risk group predictive model for borrowers defaulting on their loans. We used a logistic regression model and the Cox proportional hazards model to develop the risk predictive model. We verified the validity of the models using a receiver operating characteristic curve and a validation dataset. The present study shows that area under the receiver operating characteristic curves is similar for the models and that the major influencing factors for defaulting on their loans are household income, whether a national grant was received, age, whether more than two accounts are overdue, field of study and the monthly repayment amount. The risk group predictive model in this study will be the basis for more efficient management of direct student loans.

Suggested Citation

  • Jun†Tae Han & Jae†Seok Choi & Myeon†Jung Kim & Jina Jeong, 2018. "Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt," Asian Economic Journal, East Asian Economic Association, vol. 32(1), pages 3-14, March.
    • Handle: RePEc:bla:asiaec:v:32:y:2018:i:1:p:3-14
    DOI: 10.1111/asej.12139
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    References listed on IDEAS

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    1. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    2. Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
    3. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    4. Chun Chang & Guanmin Liao & Xiaoyun Yu & Zheng Ni, 2014. "Information from Relationship Lending: Evidence from Loan Defaults in China," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(6), pages 1225-1257, September.
    5. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    6. Madan, Dilip B., 2014. "Modeling and monitoring risk acceptability in markets: The case of the credit default swap market," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 63-73.
    7. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    8. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    9. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    10. Okumu Argan Wekesa & Mwalili Samuel & Mwita Peter, 2012. "Modelling Credit Risk for Personal Loans Using Product-Limit Estimator," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(1), pages 22-32, January.
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    Cited by:

    1. Rasa Kanapickiene & Renatas Spicas, 2019. "Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania," Risks, MDPI, vol. 7(2), pages 1-23, June.

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