Default Predictors and Credit Scoring Models for Retail Banking

53 Pages Posted: 7 Dec 2009

See all articles by Evžen Kočenda

Evžen Kočenda

Charles University in Prague - Institute of Economic Studies; Institute of Information Theory and Automation (Czech Academy of Sciences) - Department of Econometrics; CESifo; University of Regensburg - Institute for East and Southeast European Studies; University of Michigan at Ann Arbor - The William Davidson Institute

Martin Vojtek

Charles University in Prague - CERGE-EI, a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences

Date Written: December 2009

Abstract

This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are compared in terms of efficiency and power to discriminate between low and high risk clients by employing data from a new European Union economy. We are able to detect the most important characteristics of default behavior: the amount of resources the client has, the level of education, marital status, the purpose of the loan, and the number of years the client has had an account with the bank. Both methods are robust: they found similar variables as determinants. We therefore show that parametric as well as non-parametric methods can produce successful models. We are able to obtain similar results even when excluding a key financial variable (amount of own resources). The policy conclusion is that socio-demographic variables are important in the process of granting credit and therefore such variables should not be excluded from credit scoring model specification.

Keywords: credit scoring, discrimination analysis, banking sector, pattern recognition, retail loans, CART, European Union

JEL Classification: B41, C14, D81, G21, P43

Suggested Citation

Kocenda, Evzen and Vojtek, Martin, Default Predictors and Credit Scoring Models for Retail Banking (December 2009). CESifo Working Paper Series No. 2862, Available at SSRN: https://ssrn.com/abstract=1519792 or http://dx.doi.org/10.2139/ssrn.1519792

Evzen Kocenda (Contact Author)

Charles University in Prague - Institute of Economic Studies ( email )

Opletalova St. 26
Prague, 11000
Czech Republic

HOME PAGE: http://kocenda.fsv.cuni.cz

Institute of Information Theory and Automation (Czech Academy of Sciences) - Department of Econometrics ( email )

Pod vodarenskou vezi 4
CZ-18208 Praha 8
Czech Republic

CESifo

Poschinger Str. 5
Munich, DE-81679
Germany

University of Regensburg - Institute for East and Southeast European Studies

Landshuterstr. 4
Regensburg, 93047
Germany

University of Michigan at Ann Arbor - The William Davidson Institute

724 E. University Ave.
Wyly Hall
Ann Arbor, MI 48109-1234
United States

Martin Vojtek

Charles University in Prague - CERGE-EI, a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences ( email )

Politickych veznu 7
Prague, 111 21
Czech Republic

HOME PAGE: http://www.cerge-ei.cz

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