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

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Author Info

  • Yu, Lean
  • Wang, Shouyang
  • Lai, Kin Keung
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Abstract

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.

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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 195 (2009)
Issue (Month): 3 (June)
Pages: 942-959

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Handle: RePEc:eee:ejores:v:195:y:2009:i:3:p:942-959

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Web page: http://www.elsevier.com/locate/eor

Related research

Keywords: Multicriteria decision analysis Fuzzy group decision making Intelligent agent Credit scoring Artificial intelligence;

References

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  1. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
  2. Piramuthu, Selwyn, 1999. "Financial credit-risk evaluation with neural and neurofuzzy systems," European Journal of Operational Research, Elsevier, vol. 112(2), pages 310-321, January.
  3. Gerardine DeSanctis & R. Brent Gallupe, 1987. "A Foundation for the Study of Group Decision Support Systems," Management Science, INFORMS, vol. 33(5), pages 589-609, May.
  4. Varetto, Franco, 1998. "Genetic algorithms applications in the analysis of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1421-1439, October.
  5. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(03), pages 757-770, September.
  6. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
  7. Malhotra, Rashmi & Malhotra, D. K., 2002. "Differentiating between good credits and bad credits using neuro-fuzzy systems," European Journal of Operational Research, Elsevier, vol. 136(1), pages 190-211, January.
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Citations

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Cited by:
  1. Dragos Palaghita & Bogdan Vintila, 2010. "The Role of Intelligent Agents in the Knowledge Based Economy," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 2(2), pages 94-105, June.
  2. Constantin Zopounidis & Michael Doumpos, 2013. "Multicriteria decision systems for financial problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 21(2), pages 241-261, July.
  3. Huang, Yeu-Shiang & Chang, Wei-Chen & Li, Wei-Hao & Lin, Zu-Liang, 2013. "Aggregation of utility-based individual preferences for group decision-making," European Journal of Operational Research, Elsevier, vol. 229(2), pages 462-469.
  4. Sirbiladze, Gia & Khutsishvili, Irina & Ghvaberidze, Bezhan, 2014. "Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations," European Journal of Operational Research, Elsevier, vol. 232(1), pages 169-177.

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