Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking

Fifth Scientific Conference with International Participation “Economy of Integration” ICEI 2017

10 Pages Posted: 2 Dec 2017

See all articles by Sabina Brkic

Sabina Brkic

American University in Bosnia and Herzegovina

Migdat Hodzic

International University of Sarajevo (IUS)

Enis Dzanic

American University in Bosnia and Herzegovina

Date Written: November 29, 2017

Abstract

This paper deals with the use of fuzzy logic as a support tool for evaluation of corporate client credit risk in a commercial banking environment. It defines possibilistic distribution of soft data used for corporate client credit risk assessment by applying fuzzy logic modeling, with a major goal to develop a new expert decision-making fuzzy model for evaluating credit risk of corporate clients in a bank. Currently, predicting a credit risk of companies is inaccurate and ambiguous, as well as affected by many internal and external factors that cannot be precisely defined. Unlike traditional methods for credit risk assessment, fuzzy logic can easily incorporate linguistic terms and expert opinions which makes it more adapted to cases with insufficient and imprecise hard data, as well as for modeling risks that are not fully understood. Fuzzy model of soft data, presented in this paper, is created based on expert experience of corporate lending of a commercial bank in Bosnia and Herzegovina. This market is very small and it behaves irrationally and often erratically and therefore makes the risk assessment and management decision making process very complex and uncertain which requires new methods for risk modeling to be evaluated. 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 membership functions of these soft variables. All identified soft variables can be grouped into following segments: stability, capability and readiness/willingness of the client to repay a loan. The results of this work represent a new approach for soft data usage/assessment with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment.

Keywords: fuzzy logic, credit risk, default risk, commercial banking

JEL Classification: C53, G21, G32

Suggested Citation

Brkic, Sabina and Hodzic, Migdat and Dzanic, Enis, Fuzzy Logic Model of Soft Data Analysis for Corporate Client Credit Risk Assessment in Commercial Banking (November 29, 2017). Fifth Scientific Conference with International Participation “Economy of Integration” ICEI 2017 . Available at SSRN: https://ssrn.com/abstract=3079471

Sabina Brkic

American University in Bosnia and Herzegovina ( email )

Mije Keroševića Guje 3
Sarajevo, Tuzla 71000
Bosnia and Herzegovina

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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