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

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  • 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.

Suggested Citation

  • Evzen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," William Davidson Institute Working Papers Series wp1015, William Davidson Institute at the University of Michigan.
  • Handle: RePEc:wdi:papers:2011-1015
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    Cited by:

    1. Brůha, Jan & Kočenda, Evžen, 2018. "Financial stability in Europe: Banking and sovereign risk," Journal of Financial Stability, Elsevier, vol. 36(C), pages 305-321.
    2. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    3. 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.
    4. 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, , vol. 4(1), pages 11-22.
    5. 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.
    6. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    7. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    8. Selcuk Bayraci, 2017. "Application of profit-based credit scoring models using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 3-28, December.
    9. Aneta Dzik-Walczak & Mateusz Heba, 2019. "A comparison of credit scoring techniques in Peer-to-Peer lending," Working Papers 2019-16, Faculty of Economic Sciences, University of Warsaw.
    10. 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.
    11. 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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    More about this item

    Keywords

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

    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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