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Multicriteria Decision Aid in Credit Cards Assessment

  • Constantin Zopounidis
  • Panos M. Pardalos
  • Michael Doumpos
  • Thelma Mavridou
Part of the Applied Optimization book series (APOP, volume 19)

Abstract

Credit cards constitute one of the most common forms of credit, which is mainly used by consumers to cover their daily expenses. The increasing demand for credit cards during the last two decades, has necessitated the development of evaluating systems to reduce the credit risk. Generally, decisions regarding credit card evaluation involve the acceptance or the rejection of a credit card application on the basis of the applicant’s personal and business profile, which is usually described through both quantitative and qualitative factors. The objective of this paper is to present the application of multicriteria decision aid (MCDA) in credit card evaluation. For this purpose, three preference disaggregation methodologies are applied in a sample consisting of 150 credit card applications which were submitted for consideration to the National Bank of Greece during the period 1995–1996.

Keywords

Credit cards Multicriteria decision aid Preference disaggregation 

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References

  1. Ausubet, L. (1991), “The failure of competition in credit-card market”, The American Economic Review 91, 3, 50 – 81.Google Scholar
  2. Bergeron, M., Martel, J.M. and Twarabimenye, P. (1997), “The evaluation of corporate loan applicati.ons based on the MCDA”, Journal of Euro-Asian Management (in press).Google Scholar
  3. Carter, C. and Catlett, J. (1987), “Assessing credit card applications using machine learning”, IEEE Expert, Fall, 71–79.Google Scholar
  4. Damaskos, X.S. (1997), Decision models for the evaluation of credit cards: Application of the mutticriteria method ELECTRE TRI, Masters Thesis, Technical University of Crete, Chania, Greece (in Greek).Google Scholar
  5. Devaud, J.M., Groussaud, G. and Jacquet-Lagrèze, E. (1980), “UTADIS: Une méthode de construction de fonctions d’utilité additives rendant compte de jugements globaux”, European Working Group on Multicriteria Decision Aid, Bochum.Google Scholar
  6. Dimitras, A.I., Zopounidis, G., and Hurson, C. (1995), “A multicriteria decision aid method for the assessment of business failure risk”, Foundations of Computing and Decision Sciences 20,2,99–112.Google Scholar
  7. Eisenbeis, R. (1977), “The pitfalls in the application of discriminant analysis in business, finance and economics”, The Journal of Finance 32,723–739.CrossRefGoogle Scholar
  8. Jacquet-Lagrèze, E. (1995), “An application of the UTA discriminant model for the evaluation of R & D projects”, in: P.M. Pardalos, Y. Siskos and C. Zopounidis (eds.), Advances in Multicriteria Analysis, Kluwer Academic Publishers, Dordrecht, 203–211.CrossRefGoogle Scholar
  9. Jacquet-Lagrèze, E. and Siskos, Y. (1982), “Assessing a set of additive utility functions for multicriteria decision making: The UTA method”, European Journal of Operational Research 10,151–164.CrossRefGoogle Scholar
  10. Jensen, J.L. (1992), “Using neural networks for credit scoring”, Managerial Finance 18,15–26.CrossRefGoogle Scholar
  11. Nikbakht, E. and Tafti, M.H.A (1989), “Applications of expert systems in evaluation of credit card borrowers”, Managerial Finance 15,5,19–27.CrossRefGoogle Scholar
  12. Pardalos, P.M., Siskos, Y. and Zopounidis, C. (1995), Advances in Multicriteria Analysis, Kluwer Academic Publishers, DordrechtCrossRefGoogle Scholar
  13. Slowinski, R., and Zopounidis, C. (1995), “Application of the rough set approach to evaluation of bankruptcy risk”, International Journal of Intelligent Systems in Accounting, Finance and Management 4,27–41.Google Scholar
  14. Zopounidis, C. (1987), “A multicriteria decision-making methodology for the evaluation of the risk of failure and an application”, Foundations of Control Engineering 12,1, 45–67.Google Scholar
  15. Zopounidis, C. (1997), “Multicriteria decision aid in financial management”, in: J. Barcelo (ed), Plenaries and Tutorials of EUROXV-INFORMS XXXIV Joint International Meeting, 7–31.Google Scholar
  16. Zopounidis, C., and Doumpos, M. (1997a), “Preference disaggregation methodology in segmentation problems: The case of financial distress”, in: C. Zopounidis (ed.), New Operational Approaches for Financial Modelling, Springer-Verlag, Berlin-Heidelberg, 417–439.CrossRefGoogle Scholar
  17. Zopounidis, C., and Doumpos, M. (1997b), “Developing a multicriteria decision support system for financial classification problems: The FINCLAS system”, Optimization Methods and Software (in press).Google Scholar
  18. Zopounidis, C., and Doumpos, M. (1998), “A multicriteria sorting methodology for financial classification problems”, Gestion 2000, Belgian Management Magazine: French-English (to appear).Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Constantin Zopounidis
    • 1
  • Panos M. Pardalos
    • 2
  • Michael Doumpos
    • 1
  • Thelma Mavridou
    • 1
  1. 1.Department of Production Engineering and Management, Decision Support Systems LaboratoryTechnical University of CreteChaniaGreece
  2. 2.Industrial and Systems Engineering Dept., Center of Applied OptimizationUniversity of FloridaGainesvilleUSA

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