Failure prediction with self organizing maps

Authors:
Johan Huysmans;Bart Baesens;Jan Vanthienen;Tony van Gestel
Affiliations:
Faculty of Economics and Applied Economics, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;School of Management, University of Southampton, Southampton SO17 1BJ, UK and Faculty of Economics and Applied Economics, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Credit Risk Modelling, Group Risk Management, Dexia Group, Square Meeus 1, B-1000 Brussel, Belgium and Department of Electrical Engineering, ESAT-SCD-SISTA, Katholieke Universiteit Leuven, Kasteel ...;Department of Electrical Engineering, ESAT-SCD-SISTA, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven (Heverlee), Belgium
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

Citing 4
Cited 7

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Abstract

The significant growth of consumer credit has resulted in a wide range of statistical and non-statistical methods for classifying applicants in 'good' and 'bad' risk categories. Self organizing maps (SOMs) exist since decades and although they have been used in various application areas, only little research has been done to investigate their appropriateness for credit scoring. This is mainly due to the unsupervised character of the SOM's learning process. In this paper, the potential of SOMs for credit scoring is investigated. First, the powerful visualization capabilities of SOMs for exploratory data analysis are discussed. Afterwards, it is shown how a trained SOM can be used for classification and how the basic SOM-algorithm can be integrated with supervised techniques like the multi-layered perceptron. Two different methods of integration are proposed. The first technique consists of improving the predictive power of individual neurons of the SOM with the aid of supervised classifiers. The second integration method is similar to a stacking model in which the output of a supervised classifier is entered as an input variable for the SOM. Classification accuracy of both approaches is benchmarked with results reported previously.