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http://dx.doi.org/10.7465/jkdi.2016.27.5.1293

Developing the credit risk scoring model for overdue student direct loan  

Han, Jun-Tae (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.27, no.5, 2016 , pp. 1293-1305 More about this Journal
Abstract
In this paper, we develop debt collection predictive models for the person in arrears by utilizing the direct loan data of the Korea Student Aid Foundation. We suggest credit risk scorecards for overdue student direct loan using the developed 3 models. Model 1 is designed for 1 month overdue, Model 2 is designed for 2 months overdue, and Model 3 is designed for overdue over 2 months. Model 1 shows that the major influencing factors for the delinquency are overdue account, due data for payment, balance, household income. Model 2 shows that the major influencing factors for delinquency loan are days in arrears, balance, due date for payment, arrears. Model 3 shows that the major influencing factors for delinquency are the number of overdue in recent 3 months, due data for payment, overdue account, arrears. The debt collection predictive models and credit risk scorecards in this study will be the basis for segmented management service and the call & collection strategies for preventing delinquency.
Keywords
Credit risk scorecard; logistic regression; student direct loan;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Altman, E. I., Haldeman, R. and Narayanan, P. (1977). Zeta analysis-a new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 29-54.   DOI
2 Choi, J. S., Han, J. T., Kim, M. J. and Jeong, J. (2015). Developing the high risk group predictive model for student direct loan default using data mining. Journal of the Korean Data & Information Science Society, 26, 1417-1426.   DOI
3 Grunert, J. and Weber, M. (2009). Recovery rates of commercial lending: Empirical evidence for German companies. Journal of Banking and Finance, 33, 505-513.   DOI
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.   DOI
5 Khieu, H. D., Mullineaux, D. J. and Yi, H. C. (2012). The determinants of bank loan recovery rates. Journal of Banking and Finance, 36, 923-933.   DOI
6 Kang, H. C., Han, S. T., Choi, J. H., Lee, S. G., Kim, E. S. and Um, I. H. (2014). Methodology of data mining for big data analysis: A case study on SAS Enterprise Miner, Free Academy, Seoul.
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.   DOI
8 Leow, M. and Crook, J. (2016). The stability of survival model parameter estimates for predicting the probability of default: Empirical evidence over the credit crisis. European Journal of Operational Research, 249, 457-464.   DOI
9 Pompe, P. P. M. and Bilderbeek, J. (2005). The prediction of bankruptcy of small-and medium-sized industrial firms. Journal of Business Venturing, 20, 847-868.   DOI
10 Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring, John Wiley & Sons, New Jersey.
11 Woo, H. S., Lee, S. H. and Cho, H. J. (2013). Building credit scoring models with various types of target variables. Journal of the Korean Data & Information Science Society, 24, 85-94.   DOI
12 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.