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

15 Pages Posted: 24 Jul 2018  

Sabina Brkic

Independent

Migdat Hodzic

International University of Sarajevo (IUS)

Enis Dzanic

American University in Bosnia and Herzegovina

Date Written: July 2, 2018

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 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.

Keywords: Soft Data, Type 2 Fuzzy Distributions, Credit Risk, Default Risk, Commercial Banking

JEL Classification: C53, G21, G32

Suggested Citation

Brkic, Sabina and Hodzic, Migdat and Dzanic, Enis, Soft Data Modeling Via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking (July 2, 2018). Available at SSRN: https://ssrn.com/abstract=3206607 or http://dx.doi.org/10.2139/ssrn.3206607

Sabina Brkic

Independent ( email )

No Address Available

Migdat Hodzic

International University of Sarajevo (IUS) ( email )

Hrasnička cesta 15
71210
Bosnia and Herzegovina

Enis Dzanic (Contact Author)

American University in Bosnia and Herzegovina ( email )

Mije Keroševića Guje 3
Sarajevo, Tuzla 71000
Bosnia and Herzegovina

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