An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support : The case of credit scoring

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

153 Scopus Citations
View graph of relations


  • Lean Yu
  • Shouyang Wang
  • Kin Keung Lai

Related Research Unit(s)


Original languageEnglish
Pages (from-to)942-959
Journal / PublicationEuropean Journal of Operational Research
Issue number3
Publication statusPublished - 16 Jun 2009


Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented. © 2007 Elsevier B.V. All rights reserved.

Research Area(s)

  • Artificial intelligence, Credit scoring, Fuzzy group decision making, Intelligent agent, Multicriteria decision analysis

Citation Format(s)