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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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    More about this item

    Keywords

    credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union;
    All these keywords.

    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 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Finance; Public Finance

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