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

Brkic, Sabina and Hodzic, Migdat and Dzanic, Enis (2018): Soft Data Modeling via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking. Forthcoming in:

Abstract

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 dis-tributions so that broader range of banking data uncertainties can be handled and combined with the corresponding hard data, which all affect banking credit deci-sion 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Soft data, Type 2 fuzzy distributions, credit risk, default risk, commercial banking
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill

References

Bank for International Settlements. (June 2006). International Convergence of Capital Measurement of Capital Measurement. Basel Committee on Banking Supervision, A Revised Framework Comprehensive Version.

Bennett, J.C., Bohoris, G.A., Aspinwall, E.M., & Hall, R.C., (1996). Risk analysis techniques and their application to software development. European Journal of Operational Research, vol. 95, (3), pp. 467-475.

Brkic, S., Hodzic, M., & Dzanic, E.,. Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking, (Nov 2017). Fifth Scientific Confe-rence with International Participation “Economy of Integration” ICEI 2017, Available at SSRN: https://ssrn.com/abstract=3079471

Gupta, Vipul K., &Celtek, S., (2001). A fuzzy expert system for small business loan processing, Journal of International Information Management: Vol. 10, Article 2. Available at: http://scholarworks.lib.csusb.edu/jiim/vol10/iss1/2

Hayden E., Porath D., (2011). Statistical Methods to Develop Rating Models, The Basel II Risk Paramenters. London: Springer, pp.1-12. Hodzic, M., (2016a). Fuzzy to Random Uncertainty Alignment, Southeast Europe Journal of Soft Computing, 5, pp. 58-66.

Hodzic, M., (2016b). Uncertainty Balance Principle, IUS Periodicals of engineering and natural sciences, 4, No.2, pp. 17-32.

Hodzic, M., (2017). Soft to Hard Data Transformation Using Uncertainty Balance Principle, International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, Springer IAT 2017: Advanced Technologies, Systems, and Applications II, pp. 785-809 . Karnik, N. N., Mendel, J. M. & Liang, Q., (1999). Type-2 fuzzy logic systems, IEEE Transac-tions on Fuzzy Systems 7 (6), pp. 643–658.

Khashei, M., &Bijari, M., &Hejazi, S.R., (2012) Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting. Soft Computing, 16, 6, pp-1091–1105.

Khashei, M., &Mirahmadi, A., (2015). A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification, International Journal of Financial Studies, 3, pp. 411-422.

Kickert, W. J. M., (1979). An example of linguistic modeling, the case of Mulder’s theory of power, in: M.M. Gupta, R.K. Ragade& R.R. Yager (eds.): Advances in Fuzzy Set Theory and Applications, North-Holland, Amsterdam.

Kosko, B., &Isaka, S., (1993). Fuzzy logic, Scientific American 269, pp.76-81.

Lando, D., (2004). Credit risk modeling: Theory and applications, Princeton Series in Finance; Princeton University Press: Princeton, NJ, USA.

Mendel, J. M., (2003). “Type-2 Fuzzy Sets: Some Questions and Answers, ”IEEE Connections, Newsletter of the IEEE Neural Networks Society 1, pp. 10–13

Mendel, J. M., (2007). “Type-2 fuzzy Sets and Systems: An Overview,” IEEE Computational Intelligence Magazine, 2, pp. 20–29.

Shang, K., Hossen, Z., (2013). Applying Fuzzy Logic to Risk Assessment and Decision-Making, Casualty Actuarial Society, Canadian Institute of Actuaries, Society of Actuaries.

Thomas, L.C. & Edelman, D.B. & Crook, J.N. (2002) Credit Scoring and its Applications, SIAM Monographs on Mathematical Modeling and Computation; SIAM: Philadelphia, PA, USA.

Viot, G., (1993). Fuzzy logic: Concepts to constructs, AI Expert, 8, pp. 26-33.

Zadeh, L. A., (1965). Fuzzy sets, Information and Control, vol. 8, pp. 338-353.

Zadeh, L. A. , (1975). "The Concept of a Linguistic Variable and Its Application to Approximate Reasoning–1,"Information Sciences, vol. 8, pp. 199–249.

Zadeh, L. A., (1978). Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems 1, pp. 3-28.

Zadeh, L. A., (1981). Possibility theory and soft data analysis, Selected papers by LotfiZadeh, pp. 515-541.

Zirakja, M.H., Samizadeh, R., (2011). Risk Analysis in E-commerce via Fuzzy Logic. Int. J. Manag. Bus. Res., 1 (3), pp. 99-112.

Wu, D., (2012). “On the Fundamental Differences Between Interval Type-2and Type-1 Fuzzy Logic Controllers”, IEEE Transactions on Fuzzy Systems, vol. 20, no. 5, pp 832-848.