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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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    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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