Investigating the Drivers of Operational Risk in the Central Branches of the Selected Banks in Tehran and Strategies for Its Mitigation
Keywords:
Operational risk, human resource weaknesses, process weaknesses, external events, system weaknessesAbstract
The main objective of the present study was to investigate the drivers of operational risk in the central branches of the selected banks in Tehran and to identify strategies for reducing such risks. In the present study, the statistical population consisted of all experts, specialists, and officials working in the headquarters and branches of the selected banks in Tehran. A random sampling method was employed in this research. To determine content validity and assess whether the data collection instrument adequately represented the content intended to be measured, 10 questionnaires were distributed among experts. Based on the collected information and considering the Content Validity Ratio (CVR) according to Lawshe’s table, the minimum acceptable validity level, given the number of evaluators, was determined to be 62%. Furthermore, Cronbach’s alpha coefficient and composite reliability were used to examine the reliability of the questionnaire. Since the Average Variance Extracted (AVE) for all variables exceeded 0.50, and the composite reliability and Cronbach’s alpha values for all variables were greater than 0.70, the measurement instrument (questionnaire) was considered valid and reliable. Four major tools for data collection include documents and records, observation, interviews, and questionnaires. In this study, valuable information was gathered through interviews with 15 managers and experts. After several stages of preliminary reviews and modifications, the final questionnaire consisting of 35 items was prepared for implementation. Descriptive and inferential statistics were used to analyze the research data. Descriptive statistics were employed to calculate measures of central tendency and dispersion, while inferential statistics were used to test the research hypotheses. Data analyses were conducted using SPSS, VisualPLS, and MINITAB software packages. Based on the findings, the significant impact of human resource weaknesses, as a driver of operational risk, on the occurrence of losses related to operational risk in the selected banks was confirmed. In addition, the substantial impact of process weaknesses, as a driver of operational risk, on the occurrence of operational risk losses in the selected banks was also confirmed. The findings further indicated that external events had a significant effect, as a driver of operational risk, on the occurrence of operational risk losses in the banking sector.
References
Abdymomunov, A., Curti, F., & Mihov, A. (2020). US Banking Sector Operational Losses and the Macroeconomic Environment. Journal of Money, Credit and Banking, 52(1), 115-144. https://doi.org/10.1111/jmcb.12661
Addo, K. A., Hussain, N., & Iqbal, J. (2021). Corporate Governance and Banking Systemic Risk: A Test of the Bundling Hypothesis. Journal of International Money and Finance, 115, 102327.
Aftan, S., & Shah, H. (2023). Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia. Brain Sciences, 13(1), 147. https://doi.org/10.3390/brainsci13010147
Ahmadi, A., & Takloo, M. (2013). Operational Risk Modeling of Banks Using Bayesian Networks. First National Conference on Monetary and Banking Management Development, Tehran.
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Ali, A. B. M. S., & Gandomi, A. H. (2023). Deep Learning Modelling Techniques: Current Progress, Applications, Advantages, and Challenges. Artificial Intelligence Review, 56(11), 13521-13617. https://doi.org/10.1007/s10462-023-10466-8
Al‐Haija, Q. A., & Droos, A. (2024). A Comprehensive Survey on Deep Learning‐based Intrusion Detection Systems in Internet of Things ( IoT ). Expert Systems, 42(2). https://doi.org/10.1111/exsy.13726
Ali, M., Shah, D., Qazi, S., Khan, I. A., Abrar, M., & Zahir, S. (2024). An Effective Deep Learning-Based Approach for Splice Site Identification in Gene Expression. Science progress, 107(3). https://doi.org/10.1177/00368504241266588
Angouraj Taghavi, M. (2025). Analysis of Banks' Operational Risks and the Role of Internal Audit in Reducing Them. Ninth International Conference on Outstanding Research in Management, Accounting, Banking, and Economics, Mashhad.
Atia, A. D. M., Vafaei, M., Tee, K. F., & Aliah, S. C. (2024). A Systematic Review of Structural Health Monitoring Using Artificial Neural Networks: From Traditional Neural Networks to Deep Learning Algorithms. https://doi.org/10.21203/rs.3.rs-5697583/v1
Bakhtiari, M., & Yaghoobpour, S. (2024). Examining the Relationship Between Risk Governance Mechanisms and Risk-Taking Behavior in the Banking Industry. Journal of Islamic Economics and Banking, 13(48), 33-59.
Begum, A., Kumar, V. D., Asghar, J., Hemalatha, D., & Arulkumaran, G. (2022). A Combined Deep CNN: LSTM With a Random Forest Approach for Breast Cancer Diagnosis. Complexity, 2022(1). https://doi.org/10.1155/2022/9299621
Chaudhary, L., Girdhar, N., Sharma, D. K., Andreu-Pérez, J., Doucet, A., & Renz, M. (2024). A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities. IEEE Transactions on Computational Social Systems, 11(3), 3550-3579. https://doi.org/10.1109/tcss.2023.3322002
Chen, W., Hussain, W., Cauteruccio, F., & Zhang, X. (2024). Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models. Computer Modeling in Engineering & Sciences, 139(1), 187-224. https://doi.org/10.32604/cmes.2023.031388
Fadaei, A., Alirezaei, A., Hashemzadeh Khorasgan, G., & Fathi Hafshejani, K. (2021). Identifying Factors Affecting Financial Risk Management in the Automotive Industry Using the DEMATEL Technique. Financial and Investment Advances, 2(3), 31-50.
Fallah Shams, M. F., & Siahkarzadeh, M. S. (2019). Identification, Explanation, and Prioritization of Barriers to Implementing Operational Risk Management in Iranian Banks. Investment Knowledge, 8(32), 171-193.
Gonzalez-Carrasco, I., Jimenez-Marquez, J. L., Lopez-Cuadrado, J. L., & Ruiz-Mezcua, B. (2019). Automatic Detection of Relationships Between Banking Operations Using Machine Learning. Information Sciences, 485, 319-346. https://doi.org/10.1016/j.ins.2019.02.030
He, Y., Zhou, Y., Qian, Y., Liu, J., Zhang, J., Liu, D., & Wu, Q. (2025). Cardioattentionnet: Advancing ECG Beat Characterization With a High-Accuracy and Portable Deep Learning Model. Frontiers in Cardiovascular Medicine, 11. https://doi.org/10.3389/fcvm.2024.1473482
Jabed, M. A., & Murad, M. A. A. (2024). Crop Yield Prediction in Agriculture: A Comprehensive Review of Machine Learning and Deep Learning Approaches, With Insights for Future Research and Sustainability. Heliyon, 10(24), e40836. https://doi.org/10.1016/j.heliyon.2024.e40836
Kumar, N. (2023). Integrating Multiple Modalities for Accurate Emotion Recognition: A Deep Learning Ensemble Approach. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets33651
Kwon, S. H., & Kim, J. H. (2021). Machine Learning and Urban Drainage Systems: State-of-the-Art Review. Water, 13(24), 3545. https://doi.org/10.3390/w13243545
Li, W., & Hsu, C.-Y. (2022). GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography. Isprs International Journal of Geo-Information, 11(7), 385. https://doi.org/10.3390/ijgi11070385
Liang, Q., Huang, J., Liang, M., & Li, J. (2024). Economic Growth Targets and Bank Risk Exposure: Evidence from China. Economic Modelling, 135, 106702. https://doi.org/10.1016/j.econmod.2024.106702
Liu, Z., Luo, H., Chen, P., Xia, Q., Gan, Z., & Shan, W. (2022). An Efficient Isomorphic CNN-based Prediction and Decision Framework for Financial Time Series. Intelligent Data Analysis, 26(4), 893-909. https://doi.org/10.3233/ida-216142
Ljubic, B., Pavlovski, M., Gillespie, A., Rubin, D. J., Collier, G., & Obradović, Z. (2022). Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions. https://doi.org/10.1101/2022.04.22.22274183
Mohammadi Khanqah, G., Hosseini, S. A., & Maboudi, H. R. (2025). Examining Factors Affecting Banking Industry Risks Using Thematic Analysis. Financial Management Strategy, 13(4), 1-36.
Mohammadi, R. (2026). Development and Validation of a Model for Evaluating the Effect of Banking Risks on the Stability of Iran's Banking System. Dynamic Management and Business Analysis, 1-18. https://www.dmbaj.com/index.php/dmba/article/view/272
Mostafaei Dolatabad, K., Azar, A., & Moghbel Baarez, A. (2018). Identifying and Analyzing Operational Risks Using Fuzzy Cognitive Mapping. Journal of Asset Management and Financing, 6(4), 1-18.
Munguía-Siu, A., Vergara, I., & Espinoza-Rodríguez, J. H. (2024). The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography. Journal of Imaging, 10(12), 329. https://doi.org/10.3390/jimaging10120329
Naderi, H., & Rastegar, M. A. (2022). Applying the Meta-Synthesis Method in Banking Operational Risk Management Methodology. Asset Management and Financing, 10(4), 115-132.
Naderi, H., Rastegar Sorkheh, M. A., Estadi, B., & Kargari, M. (2025). Predicting the Probability of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms. Asset Management and Financing, 13(4), 77-96.
Nosrati, P., Ahmadi, H., & Kamali Rad, E. (2025). Examining the Effect of Corporate Governance Mechanisms and Banks' Social Responsibility on Types of Bank Risk-Taking: Evidence from the Tehran Stock Exchange. Accounting and Management Perspective, 8(102), 264-278.
Pena, A., Patino, A., Chiclana, F., Caraffini, F., Gongora, M., Gonzalez-Ruiz, J. D., & Duque-Grisales, E. (2021). Fuzzy Convolutional Deep-Learning Model to Estimate the Operational Risk Capital Using Multi-Source Risk Events. Applied Soft Computing, 107, 107381. https://doi.org/10.1016/j.asoc.2021.107381
Pham, H. H., Khoudour, L., Crouzil, A., Zegers, P., & Velastín, S. A. (2022). Video-Based Human Action Recognition Using Deep Learning: A Review. https://doi.org/10.48550/arxiv.2208.03775
Qamar, W. U. R. (2025). Deep Learning in Intracranial EEG for Seizure Detection: Advances, Challenges, and Clinical Applications. Frontiers in Neuroscience, 19. https://doi.org/10.3389/fnins.2025.1677898
Ramaswamy, S. L., & Jayakumar, C. (2023). Review on Positional Significance of LSTM and CNN in the Multilayer Deep Neural Architecture for Efficient Sentiment Classification. Journal of Intelligent & Fuzzy Systems, 45(4), 6077-6105. https://doi.org/10.3233/jifs-230917
Ranjbarzadeh, R., Dorosti, S., Ghoushchi, S. J., Caputo, A., Tırkolaee, E. B., Ali, S. S., Arshadi, Z., & Bendechache, M. (2023). Breast Tumor Localization and Segmentation Using Machine Learning Techniques: Overview of Datasets, Findings, and Methods. Computers in Biology and Medicine, 152, 106443. https://doi.org/10.1016/j.compbiomed.2022.106443
Roy, B., Malviya, L., Kumar, R., Mal, S., Kumar, A., Bhowmik, T., & Hu, J. W. (2023). Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals. Diagnostics, 13(11), 1936. https://doi.org/10.3390/diagnostics13111936
Sadeghi Amroabadi, B., & Yazdani, M. (2020). Identification and Assessment of Operational Risk in the Process of Information Management, Communications, and Customer Service Activities of Ansar Bank. Journal of Islamic Economics and Banking, 9(31), 221-246.
Sadia, H., Farhan, S., Haq, Y. U., Sana, R., Mahmood, T., Bahaj, S. A., & Khan, A. R. (2024). Intrusion Detection System for Wireless Sensor Networks: A Machine Learning Based Approach. IEEE Access, 12, 52565-52582. https://doi.org/10.1109/access.2024.3380014
Salih, A. A., Ameen, S. Y., Zeebaree, S. R. M., Sadeeq, M. A. M., Kak, S. F., Omar, N., Ibrahim, I. M., Yasin, H. M., Rashid, Z. N., & Ageed, Z. S. (2021). Deep Learning Approaches for Intrusion Detection. Asian Journal of Research in Computer Science, 50-64. https://doi.org/10.9734/ajrcos/2021/v9i430229
Selvarani, R. V., & Jose, P. S. H. (2023). A Label-Free Marker Based Breast Cancer Detection Using Hybrid Deep Learning Models and Raman Spectroscopy. Trends in Sciences, 20(4), 6299. https://doi.org/10.48048/tis.2023.6299
Shafiei, S., Khan-Mohammadi, M. H., Karami, A., & Gharghi, M. (2022). The Role of Operational Risk Event Databases in Bank Risk Management. Financial and Investment Advances, 3(6), 153-178.
Shoja, M., Kharazmi, O. A., & Ajza Shokouhi, M. (2021). Prioritizing and Systematically Examining the Relationships Among Dimensions of Human Resource Operational Risk. Improvement and transformation management studies, 30(99), 103-133.
Wang, J., Sun, P., Chen, L., Yang, J., Liu, Z., & Lian, H. (2023). Recent Advances of Deep Learning in Geological Hazard Forecasting. Computer Modeling in Engineering & Sciences, 137(2), 1381-1418. https://doi.org/10.32604/cmes.2023.023693
Wang, X., Ren, Y., Luo, Z., He, W., Hong, J., & Huang, Y. (2023). Deep Learning-Based EEG Emotion Recognition: Current Trends and Future Perspectives. Frontiers in psychology, 14. https://doi.org/10.3389/fpsyg.2023.1126994
Wu, Y. (2024). Deep Learning for Cardiovascular Disease Prediction: Recent Advances, Challenges and Future Directions. Theoretical and Natural Science, 62(1), 24-32. https://doi.org/10.54254/2753-8818/62/20241458
Zhang, X., Guo, F., Chen, T., Pan, L., Beliakov, G., & Wu, J. Z. (2023). A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research. Journal of Theoretical and Applied Electronic Commerce Research, 18(4), 2188-2216. https://doi.org/10.3390/jtaer18040110
Zhang, Z., Li, G., Xu, Y., & Tang, X. (2021). Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics, 11(8), 1402. https://doi.org/10.3390/diagnostics11081402
Zheng, Y., Xu, Z., & Xiao, A. (2023). Deep Learning in Economics: A Systematic and Critical Review. Artificial Intelligence Review, 56(9), 9497-9539. https://doi.org/10.1007/s10462-022-10272-8
Zhou, F., Qi, X., Xiao, C., & Wang, J. (2021). MetaRisk: Semi-Supervised Few-Shot Operational Risk Classification in Banking Industry. Information Sciences, 552, 1-16. https://doi.org/10.1016/j.ins.2020.11.027
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