Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data
AbstractCredit 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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Bibliographic InfoPaper provided by William Davidson Institute at the University of Michigan in its series William Davidson Institute Working Papers Series with number wp1015.
Date of creation: 01 Apr 2011
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credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union;
Other versions of this item:
- Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 47(6), pages 80-98, November.
- 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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