Abstract
One of the key decision activities in financial institutions is to assess the credit-worthiness of an applicant for a loan, and thereupon decide whether or not to grant the loan. Many classification methods have been suggested in the credit-scoring literature to distinguish good payers from bad payers. Especially neural networks have received a lot of attention. However, a major drawback is their lack of transparency. While they can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available, which hinders their acceptance by practitioners. Therefore, we have, in earlier work, proposed a two-step process to open the neural network black box which involves: (1) extracting rules from the network; (2) visualizing this rule set using an intuitive graphical representation. In this paper, we will focus on the second step and further investigate the use of two types of representations: decision tables and diagrams. The former are a well-known representation originally used as a programming technique. The latter are a generalization of decision trees taking on the form of a rooted, acyclic digraph instead of a tree, and have mainly been studied and applied by the hardware design community. We will compare both representations in terms of their ability to compactly represent the decision knowledge extracted from two real-life credit-scoring data sets.
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References
AT&T Laboratories (2003). Graphviz, graph-drawing tool. http://www.research.att.com/sw/tools/graphviz/.
Baesens, B., Mues, C., Setiono, R., De Backer, M., and Vanthienen, J. (2003a). Building intelligent credit scoring systems using decision tables. In Proceedings of the Fifth International Conference on Enterprise Information Systems (ICEIS’2003), pages 19–25, Angers, France.
Baesens, B., Setiono, R., Mues, C., and Vanthienen, J. (2003b). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3):312–329.
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., and Vanthienen, J. (2003c). Benchmarking state of the art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6):627–635.
Bryant, R. (1986). Graph-based algorithms for boolean function manipulation. IEEE Transactions on Computers, C-35(8):677–691.
David, R., Edelman, D., and Gammerman, A. (1992). Machine learning algorithms for credit-card applications. IMA Journal of Mathematics Applied In Business and Industry, 4:43–51.
Gansner, E. R., Koutsofios, E., North, S. C., and Vo, K. P. (1993). A technique for drawing directed graphs. IEEE Transactions on Software Engineering, 19(3):214–230.
Henley, W. and Hand, D. (1997). Construction of a knearest neighbour credit-scoring system. IMA Journal of Mathematics Applied In Business and Industry, 8:305–321.
Horiyama, T. and Ibaraki, T. (2002). Ordered binary decision diagrams as knowledge-bases. Artificial Intelligence, 136(2):189–213.
Kam, T., Villa, T., Brayton, R. K., and Sangiovanni-Vincentelli, A. L. (1998). Multi-valued decision diagrams: Theory and applications. International Journal on Multiple-Valued Logic, 4(1–2):9–62.
Kohavi, R. (1996). Wrappers for Performance Enhancement and Oblivious Decision Graphs. PhD thesis, Department of Computer Science, Stanford University.
Long, D. (2003). OBDD package. http://www-2.cs.cmu.edu/~modelcheck/bdd.html.
Miller, D. and Drechsler, R. (1998). Implementing a multivalued decision diagram package. In Proceedings of the International Symposium on Multiple-Valued Logic, pages 52–57, Fukuoka, Japan.
Prologa (2003). Decision table modelling tool. http://www.econ.kuleuven.ac.be/prologa/.
Santos-Gomez, L. and Darnel, M. (1992). Empirical evaluation of decision tables for constructing and comprehending expert system rules. Knowledge Acquisition, 4:427–444.
Steenackers, A. and Goovaerts, M. (1989). A credit scoring model for personal loans. Insurance: Mathematics and Economics, 8:31–34.
Thomas, L. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to customers. International Journal of Forecasting, 16:149–172.
Vanthienen, J. and Dries, E. (1994). Illustration of a decision table tool for specifying and implementing knowledge based systems. International Journal on Artificial Intelligence Tools, 3(2):267–288.
Vanthienen, J., Mues, C., and Aerts, A. (1998). An illustration of verification and validation in the modelling phase of kbs development. Data and Knowledge Engineering, 27:337–352.
Wets, G. (1998). Decision Tables in Knowledge-Based Systems: Adding Knowledge Discovery and Fuzzy Concepts to the Decision Table Formalism. PhD thesis, Department of Applied Economic Sciences, Catholic University of Leuven, Belgium.
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Mues, C., Huysmans, J., Vanthienen, J., Baesens, B. (2006). Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables and Diagrams. In: Seruca, I., Cordeiro, J., Hammoudi, S., Filipe, J. (eds) Enterprise Information Systems VI. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3675-2_13
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DOI: https://doi.org/10.1007/1-4020-3675-2_13
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