Proposing a Data Governance Model for Fraud Detection in Executive Agencies Based on Federated Learning in a Cloud Computing Environment

Document Type : Original Article

Authors

1 Ph.D. Student Department of Information Technology Management, Ki.C., Islamic Azad University , Kish, Iran, https://orcid.org/0009-0009-4675-360X

2 Associate Prof., Department of Management, Shi.C., Islamic Azad University, Shiraz, Iran, https://orcid.org/0000-0003-2067-4371

3 Assistant Prof., Department of computer, YI.C., Islamic Azad University, Tehran, Iran, https://orcid.org/0000-0002-9415-6609

4 Associate Prof., Department of Information Technology Management, SR.C. Islamic Azad University, Tehran, Iran, https://orcid.org/0009-0003-6839-4288

10.22091/jemsc.2026.14081.1309

Abstract

With the growing volume of data interactions and increasing complexity of oversight in executive agencies, the need for innovative approaches to fraud detection and data governance has become more pressing than ever. Given that data is stored in a distributed manner, cloud computing emerges as an effective solution. However, security concerns hinder direct data exchange between organizations. To address this challenge, the present study proposes a decentralized, data-driven governance model based on federated learning and cloud infrastructure. In this model, each organization preprocesses its data at the edge and extracts fraud-related features. These results are then transmitted to a central server, where deep learning techniques are used to predict new inter-organizational fraud patterns. This approach preserves confidentiality and enables collaborative analysis without requiring data aggregation. Experimental results show that the proposed method reduces computational complexity by 60% and achieves a fraud detection accuracy of 97.6%, demonstrating its high effectiveness in multi-organizational and distributed environments.

Keywords

Main Subjects

References

Adejumo, A., & Ogburie, C. (2025). Forensic accounting in financial fraud detection: Trends and challenges. International Journal of Science and Research Archive, 14, 1219-1232. https://doi.org/10.30574/ijsra.2025.14.3.1209.
Ahmad, M., Ahmed, Z., Alvarado, R., Hussain, N., & Khan, S. A. (2024). Financial development, resource richness, eco-innovation, and sustainable development: Does geopolitical risk matter? Journal of Environmental Management, 351, 119824. https://doi.org/10.1016/j.jenvman.2023.119824.
Alazzabi, W. Y. E., Mustafa, H., & Karage, A. I. (2023). Risk management, top management support, internal audit activities and fraud mitigation. Journal of Financial Crime, 30(2), 569-582.  https://doi.org/10.1108/JFC-11-2019-0147
Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., . . . Saif, A. (2022). Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences, 12(19), 9637. https://doi.org/10.3390/app12199637
Almazroi, A. A., & Ayub, N. (2023). Online payment fraud detection model using machine learning techniques. IEEE Access, 11, 137188-137203. https://doi.org/10.1109/ACCESS.2023.10341223
Andayani, W., & Wuryantoro, M. (2023). Good corporate governance, corporate social responsibility and fraud detection of financial statements. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(5), 9. https://doi.org/10.26668/businessreview/2023.v8i5.1051
Ashtiani, M. N., & Raahemi, B. (2021). Intelligent fraud detection in financial statements using machine learning and data mining: A systematic literature review. IEEE Access, 10, 72504-72525 https://doi.org/10.1109/ACCESS.2021.3080243
Bello, H. O., Ige, A. B., & Ameyaw, M. N. (2024). Deep learning in high-frequency trading: Conceptual challenges and solutions for real-time fraud detection. World Journal of Advanced Engineering Technology and Sciences, 12(02), 035-046. https://doi.org/10.5281/zenodo.10812284
Bello, O. A., Folorunso, A., Ejiofor, O. E., Budale, F. Z., Adebayo, K., & Babatunde, O. A. (2023). Machine learning approaches for enhancing fraud prevention in financial transactions. International Journal of Management Technology, 10(1), 85-108.
Canton, H. (2021). Organisation for economic co-operation and development—OECD. In The Europa Directory of International Organizations 2021 (pp. 677-687). Routledge.
Das, R., Sirazy, M., Khan, R. S., & Rahman, S. (2023). A collaborative intelligence (ci) framework for fraud detection in us federal relief programs. Applied Research in Artificial Intelligence and Cloud Computing, 6(9), 47-59. https://doi.org/10.5281/zenodo.10812282
Dorostkar Navaei, Y., Rezvani, M. H., & Eftekhari Moghadam, A. M. (2025). Identifying key influencers in social networks: A new neighborhood-based method. Journal of Soft Computing and Information Technology, 14(1), 11-26. https://doi.org/10.22034/jscit.2025.219059
Du, J. (2018). Understanding of object detection based on CNN family and YOLO. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1004/1/012029
Favour, A. A. (2022, September 2025). Regulatory compliance challenges in cloud-based AI fraud detection systems. ReseachGate. www.researchgate.net/publication/391454540
Fu, Z. (2022). Check for updates stacking model for financial fraud detection with synthetic data. In The Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022). https://doi.org/10.2991/978-94-6463-030-5_8
Ha, W., Gang, S., Navaei, Y. D., Gezawa, A. S., & Nanehkaran, Y. A. (2025). Ordered clustering-based semantic music recommender system using deep learning selection. Computers, Materials & Continua, 83(2), https://doi.org/10.32604/cmc.2025.061343.
Halbouni, S. S., Obeid, N., & Garbou, A. (2016). Corporate governance and information technology in fraud prevention and detection: Evidence from the UAE. Managerial Auditing Journal, 31(6/7), 589-628. https://doi.org/10.1108/MAJ-12-2014-1118
Hassan, S. W. U., Kiran, S., Gul, S., Khatatbeh, I. N., & Zainab, B. (2025). The perception of accountants/auditors on the role of corporate governance and information technology in fraud detection and prevention. Journal of Financial Reporting and Accounting, 23(1), 5-29. https://doi.org/10.1108/JFRA-05-2023-0235
He, Fen, et al. (2021). Applications of deep learning techniques for pedestrian detection in smart environments: A comprehensive study. Journal of Advanced Transportation, 2021(1), 5549111. https://doi.org/10.1155/2021/5549111
Islam, S., Haque, M. M., & Karim, A. N. M. R. (2024). A rule-based machine learning model for financial fraud detection. International Journal of Electrical & Computer Engineering (2088-8708), 14(1).  https://doi.org/10.11591/ijece.v14i1.pp1-10
Kanamori, S., Abe, T., Ito, T., Emura, K., Wang, L., Yamamoto, S., . . . Nojima, R. (2022). Privacy-preserving federated learning for detecting fraudulent financial transactions in japanese banks. Journal of Information Processing, 30, 789-795. https://doi.org/10.2197/ipsjjip.30.789
Katari, A., & Ankam, M. (2022). Data governance in multi-cloud environments for financial services: Challenges and solutions. Educational Research (IJMCER), 4(1), 339-353. https://doi.org/10.6731/TPCC_proceedings.003b-001-R5-00009062
Mirfallah Lialestani, M., Khamseh, A., Radfar, R., (2021). Digital transformation model, based on grounded theory. Journal of Information Systems and Telecommunication, 9(4). https://doi.org/10.58278/2345-2775-9-4-9
Moghadam, S. S., Ghorbani, A., & Forouzesh, R. (2021). Review and analysis of INTOSAI standards for good governance. International Journal of Political Science, 11(3), 63-76. https://doi.org/10.22034/ijps.2021.270134.1049
Mohanty, B., & Mishra, S. (2023). Role of artificial intelligence in financial fraud detection. Academy of Marketing Studies Journal, 27(S4). https://doi.org/10.54615/AMSJ.2023.S4.001
Mubalaike, A. M., & Adali, E. (2018). Deep learning approach for intelligent financial fraud detection system. In The 2018 3rd International Conference on Computer Science and Engineering (UBMK). https://doi.org/ 10.1109/UBMK.2018.8566574
Myalil, D., Rajan, M., Apte, M., & Lodha, S. (2021). Robust collaborative fraudulent transaction detection using federated learning. In The 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). https://doi.org/10.1109/ICMLA52953.2021.00110
O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv, 1511.08458. https://doi.org/10.48550/arXiv.1511.08458
Pamisetty, V. (2023, December 15). Leveraging AI, big data, and cloud computing for enhanced tax compliance, fraud detection, and fiscal impact analysis in government financial management. Fraud Detection, and Fiscal Impact Analysis in Government Financial Management. https://doi.org/10.21275/SR23122164932
Pamisetty, V., Pandiri, L., Annapareddy, V. N., & Sriram, H. K. (2022, June 15). Leveraging AI, machine learning, and big data for enhancing tax compliance, fraud detection, and predictive analytics in government financial management. Machine Learning, And Big Data For Enhancing Tax Compliance, Fraud Detection, And Predictive Analytics In Government Financial Management. https://doi.org/  10.21275/SR22615175938
Paulraj, B. (2024). Machine learning approaches for credit card fraud detection: A comparative analysis and the promise of 1D convolutional neural networks. In The 2024 7th International Conference on Information and Computer Technologies (ICICT). https://doi.org/10.1109/ICICT59662.2024.00035
Rehan, H. (2021). Leveraging AI and cloud computing for Real-Time fraud detection in financial systems. Journal of Science & Technology, 2(5), 127. https://doi.org/10.52451/jst.v2i5.127
Rivandi, E., & Jamili Oskouei, R. (2025). A novel approach for developing intrusion detection systems in mobile social networks. Journal of Soft Computing and Decision Analytics, 3(1), 158-170. https://doi.org/ 10.31181/jscda31202535
Salmanov, T. (2025). Enhancing corporate governance via machine learning and statistical tools for fraud detection. Advances in Corporate Governance, 2(1), 6. https://doi.org/10.54616/acg.2025.02106
Samuel, A. (2023). Enhancing financial fraud detection with AI and cloud-based big data analytics: Security implications. SSRN 5273292. https://doi.org/10.2139/ssrn.5273292
Shi, F., & Zhao, C. (2023). Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information. Finance Research Letters, 58, 104458. https://doi.org/10.1016/j.frl.2023.104458
Stojanović, B., & Božić, J. (2022). Robust financial fraud alerting system based in the cloud environment. Sensors, 22(23), 9461. https://doi.org/10.3390/s22239461
Upreti, K., Vats, P., Srinivasan, A., Daya Sagar, K., Mahaveerakannan, R., & Charles Babu, G. (2025). Detection of banking financial frauds using hyper-parameter tuning of DL in cloud computing environment. International Journal of Cooperative Information Systems, 34(01), 2350024. https://doi.org/ 10.1142/S0218843023500248
Wu, J. (2017). Introduction to convolutional neural networks. National key lab for novel software technology. Nanjing University. China, 5(23), 495. https://doi.org/10.13140/RG.2.2.36353.22883
Yandrapalli, V. (2024). AI-powered data governance: A cutting-edge method for ensuring data quality for machine learning applications. In The 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE). https://doi.org/10.1109/ICETITE57412.2024.00023
Yildirim, P. (2015). Filter based feature selection methods for prediction of risks in hepatitis disease. International Journal of Machine Learning and Computing, 5(4), 258. https://doi.org/10.7763/IJMLC.2015.V5.525
Zhang, R., Shu, H., & Navaei, Y. D. (2022). Load balancing in edge computing using integer linear programming based genetic algorithm and multilevel control approach. Wireless Communications and Mobile Computing, 2022(1), 6125246. https://doi.org/10.1155/2022/612524
Zhou, H., Deng, Z., Xia, Y., & Fu, M. (2016). A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing, 216, 208-215. https://doi.org/10.1016/j.neucom.2016.07.036
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