Using neural network rule extraction and decision tables for credit-risk evaluation


Baesens, Bart, Setiono, Rudy, Mues, Christophe and Vanthienen, Jan (2003) Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49, (3), 312-329. (doi:10.1287/mnsc.49.3.312.12739).

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Description/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.

Item Type: Article
ISSNs: 0025-1909 (print)
Related URLs:
Keywords: credit-risk evaluation, neural networks, decision tables, classification
Subjects: H Social Sciences > HG Finance
Divisions: University Structure - Pre August 2011 > School of Management
ePrint ID: 35975
Date Deposited: 24 May 2006
Last Modified: 27 Mar 2014 18:22
URI: http://eprints.soton.ac.uk/id/eprint/35975

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