Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation

TLDR

Credit‑risk evaluation is a challenging problem in financial analysis, with many classification methods proposed; neural networks are popular for their predictive accuracy but lack explainability, making it difficult to justify decisions. This study analyzes three real‑life credit‑risk datasets using neural‑network rule‑extraction techniques and explores visualizing the extracted rules as decision tables. The authors extract interpretable rules from trained neural networks and present them as compact decision tables for intuitive consultation. The results demonstrate that neural‑network rule extraction combined with decision tables provides powerful, user‑friendly decision‑support tools for credit‑risk evaluation.

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.