Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Developing the high risk group predictive model for student direct loan default using data mining

데이터마이닝을 이용한 학자금 대출 부실 고위험군 예측모형 개발

  • 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)
  • 최재석 (한국장학재단 통계분석팀) ;
  • 한준태 (한국장학재단 통계분석팀) ;
  • 김면중 (한국장학재단 통계분석팀) ;
  • 정진아 (한국장학재단 통계분석팀)
  • Received : 2015.06.15
  • Accepted : 2015.08.24
  • Published : 2015.11.30
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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.

본 연구는 한국장학재단의 2012-2014년간 일반 학자금 대출 자료를 활용하여 부실채권 보유 및 신용유의자로 분류될 수 있는 위험요인들을 파악하고, 부실 고위험군 예측모형을 개발했다. 예측모형 개발은 데이터마이닝 방법 중 의사결정나무 분석을 적용하였으며, 분석 패키지는 SAS Enterprise Miner 13.2를 활용했다. 개발된 모형은 25가지의 그룹으로 세분화 했으며, 부실 위험군에 영향을 미치는 주요 요인은 소득분위, 국가장학금 수혜유무, 나이, 연체계좌 보유 이력, 대학구분 (학부/대학원), 전공 계열, 월평균 상환액이 주요 요인으로 나타났다. 본 연구에서 개발된 부실 고위험군 예측모형은 장기연체로 인한 부실채권 발생 및 신용유의자 발생 예방을 위한 세분화된 관리서비스 제공을 위한 기초자료가 될 수 있을 것이다.

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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. https://doi.org/10.1016/S0278-4254(98)10018-2
  2. Feldman, D. and Gross, S. (2005). Mortgage default: Classification tree analysis. Journal of Real Estate Finance and Economics, 30, 369-396. https://doi.org/10.1007/s11146-005-7013-7
  3. Hong, C. S. and Bang, G. (2008). Modified Kolmogorov-Smirnov statistic for credit evaluation. The Korean Journal of Applied Statistics, 21, 1065-1075. https://doi.org/10.5351/KJAS.2008.21.6.1065
  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. https://doi.org/10.5351/KJAS.2012.25.2.261
  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. https://doi.org/10.7465/jkdi.2013.24.1.161
  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. https://doi.org/10.7465/jkdi.2013.24.3.411
  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. https://doi.org/10.1016/S0169-2070(00)00034-0
  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. https://doi.org/10.1016/S0950-7051(01)00110-1
  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.

Cited by

  1. Developing the credit risk scoring model for overdue student direct loan vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1293