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Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data

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Listed author(s):
  • Evzen Kocenda

    ()

  • Martin Vojtek

    ()

Abstract

Credit to the private sector has risen rapidly in European emerging markets but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic we construct two credit risk models based on logistic regression and Classification and Regression Trees. Both methods are comparably efficient and detect similar financial and socio-economic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources) that performs very well. This way we confirm significance of socio-demographic variables and link our results with specific issues characteristic to new EU members.

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File URL: http://deepblue.lib.umich.edu/bitstream/2027.42/133030/1/wp1015.pdf
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Paper provided by William Davidson Institute at the University of Michigan in its series William Davidson Institute Working Papers Series with number wp1015.

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Length: pages
Date of creation: 01 Apr 2011
Handle: RePEc:wdi:papers:2011-1015
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Keywords: credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union;

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Find related papers by JEL classification:
  • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
  • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
  • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
  • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
  • P43 - Economic Systems - - Other Economic Systems - - - Finance; Public Finance

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Cited by:

  1. 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.
  2. Natalia Nehrebecka, 2016. "Approach to the assessment of credit risk for non-financial corporations. Evidence from Poland," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Combining micro and macro data for financial stability analysis, volume 41 Bank for International Settlements.
  3. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris West - Nanterre la Defense, EconomiX.
  4. Martin Rezac & Frantisek Rezac, 2011. "How to Measure the Quality of Credit Scoring Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 486-507, November.
  5. Su-Lien Lu & Ming-Chun Wang, 2012. "How to Measure the Credit Risk of Housing Loans: Evidence from a Taiwanese Bank," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(0), pages 122-138, July.
  6. 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.
  7. Sanela Pasic & Adisa Omerbegovic Arapovic, 2016. "What Triggers Loan Repayment Failure of Consumer Loans Evidence from Bosnia and Herzegovina," Eurasian Journal of Business and Management, Eurasian Publications, vol. 4(1), pages 11-22.
  8. 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.
  9. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris West - Nanterre la Defense, EconomiX.
  10. Gabriela Kuvikova, 2015. "Does Loan Maturity Matter in Risk-Based Pricing? Evidence from Consumer Loan Data," CERGE-EI Working Papers wp538, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  11. Hela Ben hassine khalladi, 2017. "Financial crises management by the International Monetary Fund: Was external and public debt sustainable ?," Economics Bulletin, AccessEcon, vol. 37(1), pages 118-136.
  12. Dorfleitner, G. & Just-Marx, S. & Priberny, C., 2017. "What drives the repayment of agricultural micro loans? Evidence from Nicaragua," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 89-100.
  13. Gabriela Kuvikova, 2015. "Loans for Better Living: The Role of Informal Collateral," CERGE-EI Working Papers wp541, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  14. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris West - Nanterre la Defense, EconomiX.
  15. 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.
  16. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research Department.
  17. Enrique Marshall, 2015. "Reflexiones sobre la Práctica del Ahorro en Chile," Economic Policy Papers Central Bank of Chile 54, Central Bank of Chile.

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