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Default Predictors and Credit Scoring Models for Retail Banking

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  • Evžen Kocenda
  • Martin Vojtek

Abstract

This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are compared in terms of efficiency and power to discriminate between low and high risk clients by employing data from a new European Union economy. We are able to detect the most important characteristics of default behavior: the amount of resources the client has, the level of education, marital status, the purpose of the loan, and the number of years the client has had an account with the bank. Both methods are robust: they found similar variables as determinants. We therefore show that parametric as well as non-parametric methods can produce successful models. We are able to obtain similar results even when excluding a key financial variable (amount of own resources). The policy conclusion is that socio-demographic variables are important in the process of granting credit and therefore such variables should not be excluded from credit scoring model specification.

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File URL: http://www.cesifo-group.de/portal/page/portal/DocBase_Content/WP/WP-CESifo_Working_Papers/wp-cesifo-2009/wp-cesifo-2009-12/cesifo1_wp2862.pdf
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Bibliographic Info

Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 2862.

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Date of creation: 2009
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Handle: RePEc:ces:ceswps:_2862

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Related research

Keywords: credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union;

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References

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  1. Alberto F. Alesina & Francesca Lotti & Paolo Emilio Mistrulli, 2013. "Do Women Pay More For Credit? Evidence From Italy," Journal of the European Economic Association, European Economic Association, vol. 11, pages 45-66, 01.
  2. Ceyla Pazarbasioglu & Gudrun Johnsen & Paul Louis Ceriel Hilbers & Inci Ötker, 2005. "Assessing and Managing Rapid Credit Growth and the Role of Supervisory and Prudential Policies," IMF Working Papers 05/151, International Monetary Fund.
  3. 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.
  4. Reint Gropp & John Karl Scholz & Michelle White, 1996. "Personal Bankruptcy and Credit Supply and Demand," NBER Working Papers 5653, National Bureau of Economic Research, Inc.
  5. Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer, vol. 34(1), pages 1-34, August.
  6. 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, September.
  7. David Feldman & Shulamith Gross, 2005. "Mortgage Default: Classification Trees Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 30(4), pages 369-396, June.
  8. Avery, Robert B. & Calem, Paul S. & Canner, Glenn B., 2004. "Consumer credit scoring: Do situational circumstances matter?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 835-856, April.
  9. Long, Michael S., 1976. "Credit Screening System Selection," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 11(02), pages 313-328, June.
  10. Régis Blazy & Laurent Weill, 2006. "Why Do Banks Ask for Collateral and Which Ones ?," Working Papers of LaRGE Research Center 2006-03, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
  11. Jacobson, Tor & Lindé, Jesper & Roszbach, Kasper, 2004. "Credit Risk versus Capital Requirements under Basel II: Are SME Loans and Retail Credit Really Different?," Working Paper Series 162, Sveriges Riksbank (Central Bank of Sweden).
  12. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
  13. Jacobson, Tor & Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and Value at Risk," Working Paper Series 68, Sveriges Riksbank (Central Bank of Sweden).
  14. Jesús Saurina & Carlos Trucharte, 2007. "An assessment of Basel II procyclicality in mortgage portfolios," Banco de Espa�a Working Papers 0712, Banco de Espa�a.
  15. Marcello Bofondi & Francesca Lotti, 2006. "Innovation in the Retail Banking Industry: The Diffusion of Credit Scoring," Review of Industrial Organization, Springer, vol. 28(4), pages 343-358, June.
  16. Hasan, Iftekhar & Zazzara, Cristiano, 2006. "Pricing risky bank loans in the new Basel II environment," Research Discussion Papers 3/2006, Bank of Finland.
  17. 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 202-16, February.
  18. Robert B. Avery & Paul S. Calem & Glenn B. Canner, 2004. "Consumer credit scoring: do situational circumstances matter?," BIS Working Papers 146, Bank for International Settlements.
  19. Jesús Saurina & Carlos Trucharte, 2007. "An Assessment of Basel II Procyclicality in Mortgage Portfolios," Journal of Financial Services Research, Springer, vol. 32(1), pages 81-101, October.
  20. Apilado, Vincent P. & Warner, Don C. & Dauten, Joel J., 1974. "Evaluative Techniques in Consumer Finance—Experimental Results and Policy Implications for Financial Institutions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(02), pages 275-283, March.
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Citations

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Cited by:
  1. Fidrmuc, Jarko & Hainz, Christa, 2010. "Default rates in the loan market for SMEs: Evidence from Slovakia," Economic Systems, Elsevier, vol. 34(2), pages 133-147, June.
  2. NUCU, Anca Elena, 2011. "Managementul riscului de creditare: realizari actuale, analiza critica, sugestii
    [Credit risk management: current achievements, critical analysis, suggestions]
    ," MPRA Paper 27932, University Library of Munich, Germany.
  3. Ju, Yong Han & Sohn, So Young, 2014. "Updating a credit-scoring model based on new attributes without realization of actual data," European Journal of Operational Research, Elsevier, vol. 234(1), pages 119-126.
  4. Timotej Jagric & Vita Jagric & Davorin Kracun, 2011. "Does Non-linearity Matter in Retail Credit Risk Modeling?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(4), pages 384-402, August.

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