Presenting a Comprehensive Combined Mathematical Model for Evaluating Risk-Based Performance in Bank Branches Using ANP and VIKOR Methods
Keywords:
analytic network method, DEMATEL , fuzzy , VIKOR , multiple criteria decision-making problems , Performance evaluation , green balanced scorecard , riskAbstract
The objective of this study was to develop a comprehensive hybrid mathematical model for risk-based performance evaluation in bank branches using integrated fuzzy DEMATEL, ANP, and VIKOR methods. This applied study used a multi-criteria decision-making approach. The statistical population consisted of 460 employees of Sepah Bank in the Qazvin region, selected through purposive expert sampling. Data were collected using three field-based questionnaires: fuzzy DEMATEL to identify causal relationships among the extended Balanced Scorecard perspectives, fuzzy ANP to calculate the relative weights of the performance indicators, and fuzzy VIKOR for ranking the bank branches. After extracting candidate indicators from recent scientific literature, expert validation was conducted, and final indicators were incorporated into the hybrid evaluation model. MATLAB, Excel, and SPSS were used for computational procedures across various stages of the analysis. The DEMATEL results indicated that the sustainability perspective—particularly environmental-related risks—functions as a causal and influential dimension affecting other performance perspectives. Financial risk and customer-related risk emerged as the most influenced perspectives, while process, learning, and sustainability indicators were identified as the major influencing factors. Among the sub-indicators, interest-rate risk received the highest importance weight. Four sustainability-related risks were validated: excessive consumption of materials, excessive energy use, generation of banking service residues, and inadequate provision of electronic services. The proposed hybrid model successfully integrated qualitative and quantitative indicators to evaluate the effects of financial, operational, behavioral, and sustainability-related risks on bank branch performance. The model provides a structured and comprehensive framework for improving risk-aware decision-making and enhancing sustainable performance in banking institutions.
Downloads
References
Aduda, J., & Obondy, S. (2021). Credit risk management and efficiency of savings and credit cooperative societies: A review of literature. Journal of Applied Finance and Banking, 11(1), 99-120. https://doi.org/10.47260/jafb/1117
Alandejani, M., & Asutay, M. (2017). Nonperforming loans in the GCC banking sectors: Does the Islamic finance matter? Research in International Business and Finance, 42, 832-854. https://doi.org/10.1016/j.ribaf.2017.07.020
Aniran, S. F., Nabavi Chashmi, S. A., & Sorayyaei, A. (2025). Investigating Credit Risk Assessment Using Effective Indicators on Estimating the Relationship Between Financial Development and Economic Growth - Markov Switching Approach. Investment Knowledge Scientific Research Quarterly, 13(3), 55-78.
Arintoko, A., Badriah, L. S., Rahajuni, D., Kadarwati, N., & Priyono, R. (2024). The Impact of Macroeconomic Variables on Credit Risk: Evidence Regarding Sustainable Lending in ASEAN Countries. International Journal of Sustainable Development and Planning, 19(4), 1589-1597. https://doi.org/10.18280/ijsdp.190435
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020). Explainable Machine Learning in Credit Risk Management. Computational Economics. https://doi.org/10.1007/s10614-020-10042-0
Elahi, A. R., Mohammadipour, R., & Mohammadi, E. (2023). Designing a Meta-Heuristic Model for Credit Risk Management in Bank Refah Using an Artificial Intelligence Approach. Advertising and Sales Management, 4(1). https://asm.pgu.ac.ir/article_696645.html
Ghadimi, P., & Heavey, C. (2014). Sustainable supplier selection in medical device industry: Toward sustainable manufacturing. Procedia CIRP, 15, 165-170. https://doi.org/10.1016/j.procir.2014.06.096
Gold, S., & Awasthi, A. (2015). Sustainable global supplier selection extended towards sustainability risks from (1+n)th-tier suppliers using fuzzy AHP based approach. IFAC-PapersOnLine, 48(3), 966-971. https://doi.org/10.1016/j.ifacol.2015.06.208
Mutamimah, M., Suryani, A., & Made Dwi, A. (2024). Credit Risk Management Innovation in Bank Based on Blockchain Technology. International Congress on Information and Communication Technology, Singapore. https://doi.org/10.1007/978-981-97-3556-3_3
Naili, M., & Lahrichi, Y. (2022). The determinants of banks' credit risk: Review of the literature and future research agenda. International Journal of Finance and Economics, 27(1), 334-360. https://doi.org/10.1002/ijfe.2156
Omidvar, R., Sardari, A., & Yazdani, N. (2015). Ranking the Barriers to Green Supply Chain Management Using the DEMATEL Method (Case Study: Pars Khodro Company). New Marketing Research Scientific-Research Quarterly, 2(5, 2), 1-14.
Roshan, M., & Khodarahmi, S. (2024). Measuring Credit Risk and Capital Adequacy Considering the Size and Ownership Structure of Listed Banks in Iran Based on the Generalized Method of Moments (GMM) Panel Model. Management Accounting and Auditing Knowledge, 14(54), 313-329. https://www.jmaak.ir/article_23582.html
Rostami, M., Nabi Zadeh, A., & Shahi, Z. (2018). Investigating Factors Affecting Credit Risk of Iranian Commercial Banks with Emphasis on Bank-Specific and Macroeconomic Factors. Asset Management and Financing, 6(4, 23).
Rowshan, M., Khodarahmi, B., & Sarraf, F. (2025). Measuring Credit Risk and Capital Adequacy with Attention to the Size and Ownership Structure of Listed Iranian Companies Based on the Generalized Method of Moments (GMM) Model. Scientific-Research Quarterly of Accounting and Auditing Knowledge, 14(2, Serial 54), 313-329.
Saqafi, A., Jamal Damghaniyan, J., Sajad, S., & Khodoui, H. (2017). A comprehensive model for credit risk management in the Iranian banking system. Investment Knowledge Quarterly, 6(24). https://www.magiran.com/paper/1785349/comprehensive-model-of-credit-risk-management-in-the-banking-system-of-iran?lang=en
Temba, G. I., Kasoga, P. S., & Keregero, C. M. (2024). Impact of the quality of credit risk management practices on financial performance of commercial banks in Tanzania. SN Business Economics, 4, 38. https://doi.org/10.1007/s43546-024-00636-3
Za'eri, M. S., & Ramezani, E. A. (2011). Evaluation and Selection of Suppliers in the Green Supply Chain with a Multi-Criteria Decision Making Approach. The 2nd International and 4th National Conference on Logistics and Supply Chain, Tehran.
Zakerinia, E., & Majidi, Z. (2022). Credit Risk Management Strategies in the Banking System of the Islamic Republic of Iran. DAFFOS Publisher.
Zhang, D., Wang, J., & Ji, G. (2021). Explainable artificial intelligence in credit risk management: A review and case study. Artificial Intelligence Review, 54, 3937-3980.
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Abolfazl Zolghadr (Author); Rokhsareh Babajafari (Corresponding author); Peyman Imanzadeh (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
