FEDERATED LEARNING FOR CROSS-BANK FRAUD DEFENSE
DOI:
https://doi.org/10.34218/IJCET_16_02_016Keywords:
Federated Learning, Financial Fraud Detection, Privacy-Preserving Machine Learning, Cross-Bank Collaboration, Regulatory Compliance In BankingAbstract
Financial fraud is an ongoing menace to banks and financial institutions, calling for sophisticated detection procedures. However, regulatory constraints and data privacy issues prevent cross-bank collaboration, which undermines the efficiency of fraud-fighting efforts. Federated learning is a new solution that enables banks to collaborate on training models to identify fraud without sharing customers' sensitive information. Here, data is processed locally by each institution and shared as updates to a model in a central framework, in compliance with privacy regulations. With the help of real-time updates and collective fraud pattern detection, federated learning enhances fraud detection while maintaining the security of data. This paper explores the basic principles of federated learning, its technological progress, and its application in fraud defense. The report highlights its capacity to improve financial security, improve regulatory compliance, and provide a stronger fraud prevention environment.
References
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216. https://doi.org/10.1016/j.knosys.2021.106775
Zeng, Y., Mu, Y., Yuan, J., Teng, S., Zhang, J., Wan, J., … Zhang, Y. (2023). Adaptive Federated Learning With Non-IID Data. Computer Journal, 66(11), 2758–2772. https://doi.org/10.1093/comjnl/bxac118
Xia, Q., Ye, W., Tao, Z., Wu, J., & Li, Q. (2021). A survey of federated learning for edge computing: Research problems and solutions. High-Confidence Computing, 1(1). https://doi.org/10.1016/j.hcc.2021.100008
Zhang, K., Song, X., Zhang, C., & Yu, S. (2022, October 1). Challenges and future directions of secure federated learning: a survey. Frontiers of Computer Science. Higher Education Press Limited Company. https://doi.org/10.1007/s11704-021-0598-z
Xu, M., Ng, W. C., Lim, W. Y. B., Kang, J., Xiong, Z., Niyato, D., … Miao, C. (2023). A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges. IEEE Communications Surveys and Tutorials, 25(1), 656–700. https://doi.org/10.1109/COMST.2022.3221119
Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021, November 28). Intelligent financial fraud detection practices in post-pandemic era. Innovation. Cell Press. https://doi.org/10.1016/j.xinn.2021.100176
West, J., & Bhattacharya, M. (2016, March 1). Intelligent financial fraud detection: A comprehensive review. Computers and Security. Elsevier Ltd. https://doi.org/10.1016/j.cose.2015.09.005
Fang, H., & Qian, Q. (2021). Privacy preserving machine learning with homomorphic encryption and federated learning. Future Internet, 13(4). https://doi.org/10.3390/fi13040094
Lee, J. W., Kang, H., Lee, Y., Choi, W., Eom, J., Deryabin, M., … No, J. S. (2022). Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network. IEEE Access, 10, 30039–30054. https://doi.org/10.1109/ACCESS.2022.3159694
Mohassel, P., & Zhang, Y. (2017). SecureML: A System for Scalable Privacy-Preserving Machine Learning. In Proceedings - IEEE Symposium on Security and Privacy (pp. 19–38). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SP.2017.12
Zapechnikov, S. (2020). Privacy-Preserving Machine Learning as a Tool for Secure Personalized Information Services. In Procedia Computer Science (Vol. 169, pp. 393–399). Elsevier B.V. https://doi.org/10.1016/j.procs.2020.02.235
Papathomas, A., & Konteos, G. (2024). Financial institutions digital transformation: the stages of the journey and business metrics to follow. Journal of Financial Services Marketing, 29(2), 590–606. https://doi.org/10.1057/s41264-023-00223-x
Hamann, R., Giamporcaro, S., Johnston, D., & Yachkaschi, S. (2011). The role of business and cross-sector collaboration in addressing the “wicked problem” of food insecurity. Development Southern Africa, 28(4), 579–594. https://doi.org/10.1080/0376835X.2011.605581
Rachman Wintarto, Meiliani Meiliani, & Angelina Carolin. (2024). Digital Organisational Culture: Capturing Local Banking in Digital Transformation. International Journal of Social Science and Business, 7(4), 1022–1029. https://doi.org/10.23887/ijssb.v7i4.72306
Chan-Lau, J. A., Mitra, S., & Ong, L. L. (2012). Identifying contagion risk in the international banking system: An extreme value theory approach. International Journal of Finance and Economics, 17(4), 390–406. https://doi.org/10.1002/ijfe.1459
Machireddy, J. R. (2024). Machine Learning and Automation in Healthcare Claims Processing. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 686-701. https://doi.org/10.60087/jaigs.v6i1.335
Machireddy, Jeshwanth, Automation in Healthcare Claims Processing: Enhancing Efficiency and Accuracy(April 16, 2023). International Journal of Science and Research Archive, 2023, 09(01), 825-834, Available at SSRN: https://ssrn.com/abstract=5159747
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Prakash Manwani (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
