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How is credit scoring used to predict default in China?

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  • Ha-Thu Nguyen

Abstract

In this paper, we carry out a review of literature for both traditional and sophisticated credit assessment techniques, with a particular focus on credit scoring which is broadly used as a costeffective credit risk management tool. The objective of the paper is to present a set-up of an application credit-scoring model and to estimate such a model using an auto loan data-set of one of the largest automobile manufacturers in China. The logistic regression approach, which is widely used in credit scoring, is employed to construct our scorecard. A detailed step-by-step development process is provided, as are discussions about specific modeling issues. The paper finally shows that “married”, “house owner”, “female”, age in years, “working in public institutions, foreign, or joint venture companies”, down payment rate, and maximum months on book of current accounts negatively impact the probability of default.

Suggested Citation

  • Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    • Handle: RePEc:drm:wpaper:2015-1
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    File URL: http://economix.fr/pdf/dt/2015/WP_EcoX_2015-01.pdf
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    1. 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.
    2. 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.
    3. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    4. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    5. Lawrence, Edward C & Arshadi, Nasser, 1995. "A Multinomial Logit Analysis of Problem Loan Resolution Choices in Banking," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(1), pages 202-216, February.
    6. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    7. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    8. Emel, Ahmet Burak & Oral, Muhittin & Reisman, Arnold & Yolalan, Reha, 2003. "A credit scoring approach for the commercial banking sector," Socio-Economic Planning Sciences, Elsevier, vol. 37(2), pages 103-123, June.
    9. Hoffmann, F. & Baesens, B. & Mues, C. & Van Gestel, T. & Vanthienen, J., 2007. "Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 540-555, February.
    10. Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2006. "Internal ratings systems, implied credit risk and the consistency of banks' risk classification policies," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 1899-1926, July.
    11. C. C. Holmes & N. M. Adams, 2002. "A probabilistic nearest neighbour method for statistical pattern recognition," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 295-306, May.
    12. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    13. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    14. Robert B. Avery & Kenneth P. Brevoort & Glenn Canner, 2012. "Does Credit Scoring Produce a Disparate Impact?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 40, pages 65-114, December.
    15. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    16. Eisenbeis, Robert A., 1978. "Problems in applying discriminant analysis in credit scoring models," Journal of Banking & Finance, Elsevier, vol. 2(3), pages 205-219, October.
    17. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    18. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    19. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    20. J Banasik & J Crook & L Thomas, 2003. "Sample selection bias in credit scoring models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 822-832, August.
    21. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    22. Abdul Aziz & David C. Emanuel & Gerald H. Lawson, 1988. "Bankruptcy Prediction ‐ An Investigation Of Cash Flow Based Models[1]," Journal of Management Studies, Wiley Blackwell, vol. 25(5), pages 419-437, September.
    23. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    24. Greene, William, 1998. "Sample selection in credit-scoring models1," Japan and the World Economy, Elsevier, vol. 10(3), pages 299-316, July.
    25. Campbell, Tim S & Dietrich, J Kimball, 1983. "The Determinants of Default on Insured Conventional Residential Mortgage Loans," Journal of Finance, American Finance Association, vol. 38(5), pages 1569-1581, December.
    26. Gentry, Ja & Newbold, P & Whitford, Dt, 1985. "Classifying Bankrupt Firms With Funds Flow Components," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 146-160.
    27. J. S. Cramer, 2004. "Scoring bank loans that may go wrong: a case study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 365-380, August.
    28. J Banasik & J Crook, 2010. "Reject inference in survival analysis by augmentation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 473-485, March.
    29. Benjamin J. Keys & Tanmoy Mukherjee & Amit Seru & Vikrant Vig, 2010. "Did Securitization Lead to Lax Screening? Evidence from Subprime Loans," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(1), pages 307-362.
    30. Dinh, Thi Huyen Thanh & Kleimeier, Stefanie, 2007. "A credit scoring model for Vietnam's retail banking market," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 471-495.
    31. Robert B. Avery & Raphael W. Bostic & Paul S. Calem & Glenn B. Canner, 1996. "Credit risk, credit scoring, and the performance of home mortgages," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), vol. 82(Jul), pages 621-648, July.
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    Cited by:

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    More about this item

    Keywords

    Credit Risk; Credit Scoring; Auto Loans; Logistic Regression.;
    All these keywords.

    JEL classification:

    • G3 - Financial Economics - - Corporate Finance and Governance
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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