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

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 socioeconomic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources), which performs very well. This way, we confirm significance of sociodemographic variables and link our results with specific issues characteristic to new EU members.

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