Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables and Diagrams

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.