Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation
AbstractCredit-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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Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 49 (2003)
Issue (Month): 3 (March)
Credit-risk Evaluation; Neural Networks; Decision Tables; Classification;
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- Timotej Jagric & Vita Jagric & Davorin Kracun, 2011. "Does Non-linearity Matter in Retail Credit Risk Modeling?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(4), pages 384-402, August.
- Dejaeger, Karel & Goethals, Frank & Giangreco, Antonio & Mola, Lapo & Baesens, Bart, 2012. "Gaining insight into student satisfaction using comprehensible data mining techniques," European Journal of Operational Research, Elsevier, vol. 218(2), pages 548-562.
- S. Balcaen & H. Ooghe, 2004.
"Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?,"
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium
04/249, Ghent University, Faculty of Economics and Business Administration.
- Balcaen S. & Ooghe H., 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?," Vlerick Leuven Gent Management School Working Paper Series 2004-16, Vlerick Leuven Gent Management School.
- Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
- HERREMANS, Dorien & MARTENS, David & SÖRENSEN, Kenneth, 2014. "Looking into the minds of Bach, Haydn and Beethoven: Classification and generation of composer-specific music," Working Papers 2014001, University of Antwerp, Faculty of Applied Economics.
- HERREMANS, Dorien & MARTENS, David & SÖRENSEN, Kenneth, 2014. "Dance hit song prediction," Working Papers 2014003, University of Antwerp, Faculty of Applied Economics.
- Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006.
"Bayesian kernel based classification for financial distress detection,"
European Journal of Operational Research,
Elsevier, vol. 172(3), pages 979-1003, August.
- T. Van Gestel & B. Baesens & J. A.K. Suykens & D. Van Den Poel & D.-E. Baestaens & Bm. Willekens, 2004. "Bayesian Kernel-Based Classification for Financial Distress Detection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/247, Ghent University, Faculty of Economics and Business Administration.
- Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
- Owen P. Hall Jr. & Darrol J. Stanley, 2012. "A comparative modelling analysis of firm performance," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 43-56.
- Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
- Egmont-Petersen, M. & Feelders, A. & Baesens, B., 2005. "Confidence intervals for probabilistic network classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 998-1019, June.
- Carrizosa, Emilio & Martín-Barragán, Belén & Morales, Dolores Romero, 2011. "Detecting relevant variables and interactions in supervised classification," European Journal of Operational Research, Elsevier, vol. 213(1), pages 260-269, August.
- Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
- B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.
- Martin-Barragan, Belen & Lillo, Rosa & Romo, Juan, 2014. "Interpretable support vector machines for functional data," European Journal of Operational Research, Elsevier, vol. 232(1), pages 146-155.
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