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

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