Consumer Credit Scoring Models with Limited Data

23 Pages Posted: 2 Mar 2007

See all articles by Maja Sustersic

Maja Sustersic

Independent

Dusan Mramor

University of Ljubljana - Faculty of Economics

Jure Zupan

National Institute of Chemistry

Date Written: March 1, 2007

Abstract

In this paper we design the neural network consumer credit scoring models for financial institutions where data usually used in previous research are not available. We use extensive primarily accounting data set on transactions and account balances of clients available in each financial institution. As many of these numerous variables are correlated and have very questionable information content, we considered the issue of variable selection and the selection of training and testing sub-sets crucial in developing efficient scoring models. We used a genetic algorithm for variable selection. In dividing performing and nonperforming loans into training and testing sub-sets we replicated the distribution on Kohonen artificial neural network, however, when evaluating the efficiency of models, we used k-fold cross-validation. We developed consumer credit scoring models with error back propagation artificial neural networks and checked their efficiency against models developed with logistic regression. Considering the dataset of questionable information content, the results were surprisingly good and one of the error back propagation artificial neural network models has shown the best results. We showed that our variable selection method is well suited for the addressed problem.

Keywords: consumer credit scoring, neural networks, genetic algorithm, principle component

JEL Classification: G21, C45, C49, C53

Suggested Citation

Sustersic, Maja and Mramor, Dusan and Zupan, Jure, Consumer Credit Scoring Models with Limited Data (March 1, 2007). EFA 2007 Ljubljana Meetings Paper. Available at SSRN: https://ssrn.com/abstract=967384 or http://dx.doi.org/10.2139/ssrn.967384

Maja Sustersic

Independent ( email )

No Address Available
Slovenia

Dusan Mramor (Contact Author)

University of Ljubljana - Faculty of Economics ( email )

Kardeljeva ploscad 17
Ljubljana, 1000
Slovenia
+386 1 589 2400 (Phone)
+386 1 589 2698 (Fax)

Jure Zupan

National Institute of Chemistry ( email )

Ljubljana
Slovenia

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