Soft Data Modeling via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking

  • Sabina BrkićEmail author
  • Migdat Hodžić
  • Enis Džanić
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 59)


The work reported in this paper aims to present possibility distribution model of soft data used for corporate client credit risk assessment in commercial banking by applying Type 2 fuzzy membership functions (distributions) for the purpose of developing a new expert decision-making fuzzy model for evaluating credit risk of corporate clients in a bank. The paper is an extension of previous research conducted on the same subject which was based on Type 1 fuzzy distributions. Our aim in this paper is to address inherent limitations of Type 1 fuzzy distributions so that broader range of banking data uncertainties can be handled and combined with the corresponding hard data, which all affect banking credit decision making process. Banking experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating Type 2 fuzzy logic membership functions of these soft variables. Similar to our analysis with Type 1 fuzzy distributions, all identified soft variables can be grouped into a number of segments, which may depend on the specific bank case. In this paper we looked into the following segments: (i) stability, (ii) capability and (iii) readiness/willingness of the bank client to repay a loan. The results of this work represent a new approach for soft data modeling and usage with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment.


  1. 1.
    Bank for International Settlements: International Convergence of Capital Measurement of Capital Measurement. Basel Committee on Banking Supervision, A Revised Framework Comprehensive Version, June 2006Google Scholar
  2. 2.
    Bennett, J.C., Bohoris, G.A., Aspinwall, E.M., Hall, R.C.: Risk analysis techniques and their application to software development. Eur. J. Oper. Res. 95(3), 467–475 (1996)CrossRefGoogle Scholar
  3. 3.
    Brkic, S., Hodzic, M., Dzanic, E.: Fuzzy logic model of soft data analysis for corporate client credit risk assessment in commercial banking. In: Fifth Scientific Conference with International Participation “Economy of Integration” ICEI 2017. SSRN, November 2017.
  4. 4.
    Gupta, V.K., Celtek, S.: A fuzzy expert system for small business loan processing. J. Int. Inf. Manag. 10, Article 2 (2001).
  5. 5.
    Hayden, E., Porath, D.: Statistical methods to develop rating models. In: The Basel II Risk Paramenters. Springer, London, pp. 1–12 (2011)Google Scholar
  6. 6.
    Hodzic, M.: Fuzzy to random uncertainty alignment. Southeast Eur. J. Soft Comput. 5, 58–66 (2016)Google Scholar
  7. 7.
    Hodzic, M.: Uncertainty balance principle. IUS Period. Eng. Nat. Sci. 4(2), 17–32 (2016)Google Scholar
  8. 8.
    Hodzic, M.: Soft to hard data transformation using uncertainty balance principle. In: International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, IAT 2017. Advanced Technologies, Systems, and Applications II. Springer, pp. 785–809 (2017)Google Scholar
  9. 9.
    Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7(6), 643–658 (1999)CrossRefGoogle Scholar
  10. 10.
    Khashei, M., Bijari, M., Hejazi, S.R.: Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting. Soft Comput. 16(6), 1091–1105 (2012)CrossRefGoogle Scholar
  11. 11.
    Khashei, M., Mirahmadi, A.: A soft intelligent risk evaluation model for credit scoring classification. Int. J. Financ. Stud. 3, 411–422 (2015)CrossRefGoogle Scholar
  12. 12.
    Lando, D.: Credit Risk Modeling: Theory and Applications. Princeton Series in Finance. Princeton University Press, Princeton (2004)Google Scholar
  13. 13.
    Mendel, J.M.: Type-2 fuzzy sets: some questions and answers. In: IEEE Connections, Newsletter of the IEEE Neural Networks Society 1, pp. 10–13 (2003)Google Scholar
  14. 14.
    Mendel, J.M.: Type-2 fuzzy sets and systems: an overview. IEEE Comput. Intell. Mag. 2, 20–29 (2007)Google Scholar
  15. 15.
    Shang, K., Hossen, Z.: Applying fuzzy logic to risk assessment and decision-making, casualty actuarial society. Canadian Institute of Actuaries, Society of Actuaries (2013)Google Scholar
  16. 16.
    Thomas, L.C., Edelman, D.B., Crook, J.N.: Credit Scoring and its Applications. SIAM Monographs on Mathematical Modeling and Computation. SIAM, Philadelphia (2002)CrossRefGoogle Scholar
  17. 17.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  18. 18.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning–1. Inf. Sci. 8, 199–249 (1975)Google Scholar
  19. 19.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Zadeh, L.A.: Possibility theory and soft data analysis, Selected papers by Lotfi Zadeh, pp. 515–541 (1981)Google Scholar
  21. 21.
    Zirakja, M.H., Samizadeh, R.: Risk analysis in e-commerce via fuzzy logic. Int. J. Manag. Bus. Res. 1(3), 99–112 (2011)Google Scholar
  22. 22.
    Wu, D.: On the fundamental differences between interval type-2 and type-1 fuzzy logic controllers. IEEE Trans. Fuzzy Syst. 20(5), 832–848 (2012)CrossRefGoogle Scholar
  23. 23.
    Zimmermann, H.-J.: Fuzzy Set Theory – and Its Applications, 4th edn., pp. 158–241, 369–404. Kluwer Academic Publishers (2001)Google Scholar

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Authors and Affiliations

  1. 1.American University in Bosnia and HerzegovinaTuzlaBosnia and Herzegovina
  2. 2.International University SarajevoSarajevoBosnia and Herzegovina
  3. 3.Univerzitet u BihaćuBihaćBosnia and Herzegovina

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