To read this content please select one of the options below:

Interactive scenario analysis of banking credit risks in intuitive fuzzy space

Negar Jalilian (Yazd University, Yazd, Islamic Republic of Iran)
Seyed Mahmoud Zanjirchi (Department of Economics, Management and Accounting, Yazd University, Yazd, Islamic Republic of Iran)
Mark Goh (NUS Business School, National University of Singapore, Singapore)

Journal of Modelling in Management

ISSN: 1746-5664

Article publication date: 3 December 2019

Issue publication date: 22 January 2020

281

Abstract

Purpose

The purpose of the paper is to bring attention to documentary credits and the efforts to reduce debt obligations in credit history is recognized as an important source of uncommitted bank earnings. Credit risk has a significant impact on the stability of the banking system. This paper identifies the types of credit risk in the banking supply chain.

Design/methodology/approach

The authors model the types of credit risk using the intuitive fuzzy failure modes and effects analysis (IFMEA) and intuitive fuzzy cognitive mapping. The population of the study that is needed for the interviews and expert panels comprises senior managers and experts of a leading bank in Iran. The respondents are experienced in credit and banking risk and were selected through judgment sampling and snowballing.

Findings

The findings suggest that reducing the risks of the foreign letters of credit contracts can mitigate the risk in the agricultural sector, the specific risks of rent-to-own contracts, the risk of the long-term facilities and the specific risk of the domestic letter of credit contracts.

Originality/value

This research investigates Iran Tejart Bank’s credit risk, formulates a model of the types of credit risk present and analyzes them using the intuitive fuzzy failure modes and effects analysis and intuitive fuzzy cognitive map. Through this credit risk model, one can then facilitate risk management for better financial stability. Also, the model can be used to evaluate the risk indicators.

Keywords

Acknowledgements

This work is supported by the Iran National Science Foundation (Grant No.: 96001832).

Citation

Jalilian, N., Zanjirchi, S.M. and Goh, M. (2020), "Interactive scenario analysis of banking credit risks in intuitive fuzzy space", Journal of Modelling in Management, Vol. 15 No. 1, pp. 257-275. https://doi.org/10.1108/JM2-01-2019-0011

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

Related articles