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

Using neural network rule extraction and decision tables for credit-risk evaluation
Using neural network rule extraction and decision tables for credit-risk evaluation
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.
credit-risk evaluation, neural networks, decision tables, classification
0025-1909
312-329
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Setiono, Rudy
98ca7376-c02e-4f65-a2df-bb09cc0c6e6b
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Setiono, Rudy
98ca7376-c02e-4f65-a2df-bb09cc0c6e6b
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d

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).

Record type: Article

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.

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More information

Published date: 2003
Keywords: credit-risk evaluation, neural networks, decision tables, classification

Identifiers

Local EPrints ID: 35975
URI: https://eprints.soton.ac.uk/id/eprint/35975
ISSN: 0025-1909
PURE UUID: d4735daa-fb88-48ee-889a-d4eccdee8bc8
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 24 May 2006
Last modified: 15 Aug 2019 00:44

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