"Evaluation and Ranking of Iranian Banks Using a Composite Risk Index Based on Fuzzy Data Envelopment Analysis: A Comprehensive Approach to Risk Management"

Document Type : Original Article

Authors
hassan shamsi 1
Mirfeiz fallahshams 2
zahra pourzamani 3
Gholamreza zomorodian 2
1 Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Financial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran
10.22034/jik.2025.78560.4768
Abstract
Abstract
The Iranian banking system, as the cornerstone of a bank-centric economy, faces multiple risks that threaten its financial stability and performance. This study aims to develop a composite risk index based on Fuzzy Data Envelopment Analysis (Fuzzy DEA) to evaluate and rank 13 Iranian banks in 2021. Financial and non-financial data were extracted from the Codal system and expert opinions, analyzing credit, liquidity, operational, and market risks within an integrated fuzzy model. Using the DEMATEL technique, 33 key indicators were selected from 44 initial indicators, and the Fuzzy DEA model was implemented using LINGO software. Results showed that Pasargad and Middle East banks led with a 100% efficiency score in risk management, while Parsian, Maskan, Tejarat, and Saderat banks ranked lower due to high liquidity risk. Operational risk was identified as the most critical factor affecting efficiency. The study’s contribution lies in offering a novel approach to integrating multiple risks by modeling uncertainties through Fuzzy DEA and refining indicators with DEMATEL, enhancing evaluation accuracy compared to traditional methods like CAMELS and addressing the gap in simultaneous risk analysis in Iran’s volatile economy. These findings assist bank managers and policymakers in identifying weaknesses, improving risk management strategies, and enhancing the stability of Iran’s banking system.
Keywords: Fuzzy Data Envelopment Analysis, Composite Risk Index, Bank Ranking, Risk Management, Iranian Banking Industry.

  1. 1.       امینی جاوید، ح.، و محمدپور زرندی، م. ا. (1400). ارزیابی کارایی بانک‌های ایرانی با استفاده از تحلیل پوششی داده‌ها. فصلنامه تحقیقات مالی و بانکی, 12(3), 45–60.

    2.       تقی زاده مهرجردی، ر. (1396). تحلیل ریسک‌های اعتباری و نقدینگی در بانک‌های خصوصی ایران با استفاده از تحلیل پوششی داده‌های فازی پویا. مجله مدیریت ریسک, 8(2), 78–95.

    3.       سوری، ا. ر. (1394). تأثیر ریسک بر کارایی بانک‌های ایرانی: مطالعه‌ای مبتنی بر تحلیل پوششی داده‌ها. فصلنامه اقتصاد و بانکداری, 6(4), 112–130.

    4.       سوری، ا. ر.، گرشاسبی، ع.، و عریانی، ب. (1395). بررسی ریسک‌های بازار و عملیاتی در بانک‌های خصوصی ایران. مجله مطالعات مالی, 9(1), 88–105.

    5.       شریفی، ع.، و عزیز، ع. (1402). ارزیابی کارایی بانک‌های ایرانی با استفاده از تحلیل پوششی داده‌ها. مطالعات مدیریت ایران, 27(3), 234–250.

    6.       عالم تبریز، ا.، رجبی پور میبدی، ع.، و زارعیان، م. (1388). تأثیر شفافیت داده‌های مالی بر پایداری بانک‌ها در ایران. فصلنامه علوم اقتصادی, 5(2), 45–60.

    7.       قاسمی، م.، و همکاران. (1401). تحلیل ریسک‌های بانکی در اقتصاد پرنوسان ایران: رویکردی فازی. مجله تحقیقات اقتصادی, 20(3), 150–170.

    8.       کریمی، ع.، و شاهین، م. (1403). تأثیر فناوری دیجیتال بر کاهش ریسک عملیاتی در بانک‌های ایرانی. فصلنامه نوآوری‌های مالی, 14(1), 90–110.

    9.       محقر، ع.، حکاک، م.، و یعقوبی، ح. (1393). چالش‌های مدیریت ریسک در نظام بانکی ایران تحت تحریم‌های بین‌المللی. مجله مدیریت و اقتصاد, 10(3), 120–140.

    10.   معظمی گودرزی، م. ر. (1392). رتبه‌بندی بانک‌های ایرانی با استفاده از مدل ترکیبی DEA فازی و FAHP. فصلنامه پژوهش‌های بانکی, 7(4), 65–80.

    11.   محمدی، ر.، و توکلی، م. (1402). چالش‌های ساختاری در بانکداری ایران: پیامدها برای مدیریت ریسک. مجله پول و اقتصاد, 15(4), 112–130.

    12.   نوروزی، م.، احمدی، س.، و رضایی، ح. (1401). مدیریت ریسک و پایداری مالی در نظام بانکی ایران. فصلنامه تحقیقات مالی, 23(2), 167–185.

    13.   یزدانی، م.، رضایی، ع.، و رحیمی، ف. (1399). نقش بانک‌های ایرانی در توسعه اقتصادی: فرصت‌ها و چالش‌ها. مجله مطالعات اقتصادی, 18(4), 321–340.

    1. Altunbas, Y., Manganelli, S., & Marques-Ibanez, D. (2022). Bank risk during the financial crisis: Do business models matter? Journal of Banking & Finance, 135, Article 106123. https://doi.org/10.1016/j.jbankfin.2021.106123
    2. Basel Committee on Banking Supervision (BCBS). (2020). Principles for the sound management of operational risk. Bank for International Settlements. https://www.bis.org/publ/bcbs195.pdf
    3. Central Bank of Iran. (2021). Annual report on banking supervision. Central Bank of Iran. https://www.cbi.ir/annualreport
    4. Central Bank of Iran. (2022). Financial stability report. Central Bank of Iran. https://www.cbi.ir/financialstability
    5. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
    6. Chen, X., Skully, M., & Brown, K. (2013). Banking efficiency in China: Application of fuzzy DEA. China Economic Review, 24, 12–22. https://doi.org/10.1016/j.chieco.2012.10.003
    7. Chernobai, A., Rachev, S. T., & Fabozzi, F. J. (2021). Operational risk: A guide to Basel III and beyond. Wiley Finance. https://doi.org/10.1002/9781119756071
    8. Emrouznejad, A. (2023). Performance measurement with fuzzy data envelopment analysis. European Journal of Operational Research, 304(1), 15–30. https://doi.org/10.1016/j.ejor.2022.03.045
    9. Emrouznejad, A., & Rostamy-Malkhalifeh, M. (2014). A full ranking methodology in data envelopment analysis based on a set of dummy decision making units. Expert Systems with Applications, 41(6), 2795–2802. https://doi.org/10.1016/j.eswa.2013.10.021
    10. Giudici, P., & Raffinetti, E. (2021). Explainable AI within the digital transformation and cyber risk analytics: The case of financial services. Expert Systems with Applications, 183, Article 115397. https://doi.org/10.1016/j.eswa.2021.115397
    11. Hatami-Marbini, A. (2016). A bounded data envelopment analysis model in a fuzzy environment with an application to safety program evaluation. International Journal of Data Envelopment Analysis, 4(2), 1125–1138.
    12. Hatami-Marbini, A., Saati, S., & Tavana, M. (2021). An extended fuzzy DEA model for evaluating bank efficiency under uncertainty. European Journal of Operational Research, 290(3), 18–30. https://doi.org/10.1016/j.ejor.2020.08.012
    13. International Monetary Fund. (2022). Islamic Republic of Iran: Financial sector assessment program. IMF Country Report No. 22/123. https://www.imf.org/en/Publications/CR/Issues/2022/04/15/Iran
    14. International Monetary Fund. (2023). Global financial stability report. IMF. https://www.imf.org/en/Publications/GFSR
    15. Kao, C. (2022). Fuzzy data envelopment analysis: A review and new perspectives. Annals of Operations Research, 315(1), 451–470. https://doi.org/10.1007/s10479-021-04235-7
    16. Lin, R. J. (2023). Network fuzzy DEA for assessing Asian bank efficiency under operational and market risks. European Journal of Operational Research, 305(2), 1123–1135. https://doi.org/10.1016/j.ejor.2022.09.015
    17. Liu, J., Li, J., Wu, J., & Xia, Y. (2022). A fuzzy data envelopment analysis with application in marketing. Journal of Intelligent & Fuzzy Systems, 42(3), 1591–1602. https://doi.org/10.3233/JIFS-210826
    18. Sengupta, J. K., & Puri, S. (2024). Multi-stage fuzzy DEA for financial risk assessment. European Journal of Operational Research, 310(2), 109–123. https://doi.org/10.1016/j.ejor.2023.12.005
    19. Tamakoshi, G., & Hamori, S. (2024). Machine learning and fuzzy DEA for systemic risk prediction in Japanese banks. International Journal of Finance & Economics, 29(1), 18–30. https://doi.org/10.1002/ijfe.2456
    20. Wang, L., & Lu, D. A. (2023). A fuzzy multi-objective production planning model based on subjective satisfaction degree. Computers & Industrial Engineering, 175, Article 108898. https://doi.org/10.1016/j.cie.2022.108898
    21. Wang, L., & Zhang, Q. (2025). Dynamic fuzzy DEA for assessing Chinese bank efficiency under multiple risks. Journal of Banking & Finance, 150, 12–25. https://doi.org/10.1016/j.jbankfin.2024.106789
    22. World Bank. (2021). Iran economic monitor: Navigating turbulent waters. World Bank. https://www.worldbank.org/en/country/iran/publication/iran-economic-monitor
    23. World Bank. (2022). Global economic prospects: Middle East and North Africa. World Bank. https://www.worldbank.org/en/publication/global-economic-prospects
    24. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
    25. Zha, Y., Liang, N., Huang, M., & Bi, G. (2016). Measuring performance of Chinese banks using fuzzy dynamic network DEA. Computers & Industrial Engineering, 101, 456–468. https://doi.org/10.1016/j.cie.2016.09.025
    26. Zhou, H., & Yang, C. (2023). Integrating DEMATEL and ANP for supplier selection in the circular economy: A fuzzy approach. Sustainability, 15(6), 5234. https://doi.org/10.3390/su15065234
    27. Zhou, S., & Xu, L. (2022). A network-based fuzzy DEA for evaluating bank efficiency in Asia. Pacific Journal of Operational Research, 39(1), Article 2150021. https://doi.org/10.1142/S0217595921500219