Advertisement

A fuzzy way to evaluate the qualitative attributes in bank lending creditworthiness

  • Gisella Facchinetti
  • Giovanni Mastroleo

Abstract

In this paper we address bank evaluation of clients in lending credit, based on qualitative attributes. Till now, the banks have dodged to face this part of the lending credit. There are several reasons for this. One is the impossibility of using a statistical approach; the variables are linguistic attributes, not numbers. Another one, which we think really serious, is the difficulty of fixing which qualitative attributes are important. Every bank uses a personal contact with the client, the experts have not a unique behaviour. Here we present a sketch of our work, performed with an Italian bank, in which a fuzzy approach is used. In particular, we have used two different methods: a fuzzy expert system and a fuzzy cluster method.

Keywords

bank creditworthiness qualitative attribute fuzzy expert system fuzzy cluster 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

9 Reference

  1. [1]
    Bezdek J.C., ”Pattern recognition with fuzzy objectivefunction algorithm”, Plenum New York, 1981.Google Scholar
  2. [2]
    Duda R.-Hart P., “Pattern classification and scene analysis” Wiley, New York, 1973.Google Scholar
  3. [3]
    Facchinetti G.-Bordoni S.-Mastroleo G., “Bank Creditworthiness using Fuzzy Systems: A Comparison with a Classical Analysis Tecnique” Risk Assessment and Management in Technology, Environment and Finance. Ruan, Mario Fedrizzi and Janusz Kacprzyk Editors, pp. 472–486, Springer Verlag Press, 2000.Google Scholar
  4. [4]
    Facchinetti G.-Mastroleo G., “A Comparison between a Score Card and a Fuzzy Approach for Granting Personal Credit” Proceedings of Third Spanish-Italian Meeting on Financial Mathematics, Bilbao 2000.Google Scholar
  5. [5]
    Facchinetti G.-Cosma S.-Mastroleo G.-Ferretti R., “A fuzzy credit rating approach for small firm creditworthiness evaluation in bank lending. An Italian case.” Proceedings of ICSC 2001, Methods & Applications (CIMA 2001) Bangor, U.K. June 19–22, 2001.Google Scholar
  6. [6]
    Facchinetti G.-Giove S.-Mastroleo G., “Fuzzy expert systems and data mining for bank credit rating” Proceedings of IV Meeting Italo Spagnolo di Matematica Finanziaria ed Attuariale, pp. 303–311, Alghero 2001.Google Scholar
  7. [8]
    Gulden R, Rosignoli C, Salcioli G., “L'adozione di sistemi basati sulla conoscenza nell'area fidi degli enti creditizi” in Il Risparmio, n.2, 1994.Google Scholar
  8. [9]
    Kasabov N.K., “Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering” MIT Press, 1996.Google Scholar
  9. [10]
    Kosko, B., “Fuzzy Systems as Universal Approximators” Proc. IEEE Int. Conf. On Fuzzy Systems, pp. 1153–1162, 1992.Google Scholar
  10. [11]
    Ruozi R., “Sull'attendibilità dei bilanci e sulla loro attitudine ai fini di previsione delle insolvenze” in Bancaria, n.l, 1974.Google Scholar
  11. [13]
    von Altrock C, “Fuzzy Logic and neurofuzzy applications in business and finance.” Prentice Hall, 1997.Google Scholar
  12. [14]
    Wang L., “Fuzzy systems are universal approximators” Proc. Of Int. Conf. On Fuzzy Engineering, pp. 471–496, 1992.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Gisella Facchinetti
    • 1
  • Giovanni Mastroleo
    • 1
  1. 1.Faculty of EconomicsUniversity of Modena and Reggio EmiliaItaly

Personalised recommendations