Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation

Bart Baesens (), Rudy Setiono (), Christophe Mues () and Jan Vanthienen ()
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Bart Baesens: Department of Applied Economic Sciences, K. U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium
Rudy Setiono: Department of Information Systems, National University of Singapore, Kent Ridge, Singapore 119260, Republic of Singapore
Christophe Mues: Department of Applied Economic Sciences, K. U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium
Jan Vanthienen: Department of Applied Economic Sciences, K. U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium

Management Science, 2003, vol. 49, issue 3, 312-329

Abstract: Credit-risk evaluation is a very challenging and important management science problem in the domain of financial analysis. Many classification methods have been suggested in the literature to tackle this problem. Neural networks, especially, have received a lot of attention because of their universal approximation property. However, a major drawback associated with the use of neural networks for decision making is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available. In this paper, we present the results from analysing three real-life credit-risk data sets using neural network rule extraction techniques. Clarifying the neural network decisions by explanatory rules that capture the learned knowledge embedded in the networks can help the credit-risk manager in explaining why a particular applicant is classified as either bad or good. Furthermore, we also discuss how these rules can be visualized as a decision table in a compact and intuitive graphical format that facilitates easy consultation. It is concluded that neural network rule extraction and decision tables are powerful management tools that allow us to build advanced and userfriendly decision-support systems for credit-risk evaluation.

Keywords: Credit-risk Evaluation; Neural Networks; Decision Tables; Classification (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (93)

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