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

Evžen Kočenda and Martin Vojtek ()

No wp1015, William Davidson Institute Working Papers Series from William Davidson Institute at the University of Michigan

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.

Keywords: credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union (search for similar items in EconPapers)
JEL-codes: B41 C14 D81 G21 P43 (search for similar items in EconPapers)
Pages: pages
Date: 2011-04-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Related works:
Journal Article: Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data (2011)
Working Paper: Default Predictors and Credit Scoring Models for Retail Banking (2009)
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