Comparing Two Novel Hybrid MRDM Approaches to Consumer Credit Scoring Under Uncertainty and Fuzzy Judgments

Abstract

In the recent years, various statistical and computational intelligence or machine learning techniques have contributed to the progress of automation or semiautomation for measuring consumer credit scoring in the banking sector. However, most of the Taiwanese commercial banks still rely on seasoned staffs’ judgments on making the final approvals or rejections. To enhance the understanding and transparency of a decision support system (or model) that can assist bank staffs on making their consumer credit loan decisions—while uncertainty exist—is of high business value. One of the promising approaches is multiple rule-based decision-making (MRDM), a subfield of the hybrid multiple criteria decision-making that leverages the advantages of machine learning, soft computing, and decision methods (or techniques). The MRDM approach reveals comprehensible logics (rules or patterns) that can be justified and compared with the existing knowledge of veterans to reinforce the confidence of their judgments. Therefore, in the present study, we propose and compare two MRDM approaches in assisting decision makers on the consumer credit loan evaluations. A set of historical data from a commercial bank in Taiwan is analyzed for illustrating the plausible pros and cons of the two approaches with discussions.