Leveraging Blockchain and Federated Reinforcement Learning for Enhanced Fraud Detection in Financial Transactions
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Des 31, 2025
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Credit card fraud detection uses several technologies including machine learning (ML) and statistical analysis as well as card authentication methods. Credit card fraud is the illegal use of someone's credit card for purchase of goods or services. Comprehensive studies have shown how well ML technology generates exact prediction models to spot possible transaction fraud. Though the potential of losing the actual card is still a worry, hackers have been increasingly acquiring credit card numbers and personal information online. The growth in e-commerce has matched credit card use for online transactions, which has surged credit card theft. Complex detection systems including federated reinforcement learning (FRL) and blockchain technology have been developed to solve this challenge. Using standard pattern matching methods can make it difficult to tell real from fake transactions. A decentralised method of ML, FRL stresses user privacy and enhances anonymity and confidence in financial transactions. An original approach for teaching a credit card fraud detection model is shown in this work It makes advantage of user behaviour traits, federated reinforcement learning, and blockchain technologies. Using a smart contract between the bank and the client, the approach aims to reduce dishonest activity. By lowering the dependence on centralised data aggregation, this innovative approach guarantees user privacy protection all through model development. The research also looks at the challenges in credit card fraud detection and offers ideas for next developments.
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Alam, T. (2025). Leveraging Blockchain and Federated Reinforcement Learning for Enhanced Fraud Detection in Financial Transactions. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 6(4), 334 - 352. https://doi.org/10.62527/jitsi.6.4.492
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Referensi
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[10]. El Hlouli, F. Z., Riffi, J., Mahraz, M. A., Yahyaouy, A., El Fazazy, K., & Tairi, H. (2024). Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach. SN Computer Science, 5(1), 173.
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[12]. Shakadwipi, A. J., Jain, D. C., & Nagini, S. (2024). Fraud Detection System for Identity Crime using Blockchain Technology and Data Mining Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 247-251.
[13]. Shayan, M., Fung, C., Yoon, C. J., & Beschastnikh, I. (2020). Biscotti: A blockchain system for private and secure federated learning. IEEE Transactions on Parallel and Distributed Systems, 32(7), 1513-1525.
[14]. Zheng, W., Yan, L., Gou, C., & Wang, F. Y. (2021, January). Federated meta-learning for fraudulent credit card detection. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 4654-4660).
[15]. Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2(1-2), 55-68.
[16]. Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2022). Blockchain-enabled federated learning: A survey. ACM Computing Surveys, 55(4), 1-35.
[17]. Chatterjee, P., Das, D., & Rawat, D. (2023). Securing Financial Transactions: Exploring the Role of Federated Learning and Blockchain in Credit Card Fraud Detection.
[18]. Long, G., Tan, Y., Jiang, J., & Zhang, C. (2020). Federated learning for open banking. In Federated Learning: Privacy and Incentive (pp. 240-254). Cham: Springer International Publishing.
[19]. Qu, Y., Gao, L., Xiang, Y., Shen, S., & Yu, S. (2022). Fedtwin: Blockchain-enabled adaptive asynchronous federated learning for digital twin networks. IEEE Network, 36(6), 183-190.
[20]. Bandara, E., Shetty, S., Rahman, A., Mukkamala, R., Zhao, J., & Liang, X. (2022, January). Bassa-ml—a blockchain and model card integrated federated learning provenance platform. In 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) (pp. 753-759). IEEE.
[21]. Sam, U., Moses, G., & Olajide, T. (2023). Credit Card Fraud Detection Using Machine Learning Algorithms.
[22]. Gadekallu, T. R., Pham, Q. V., Huynh-The, T., Bhattacharya, S., Maddikunta, P. K. R., & Liyanage, M. (2021). Federated learning for big data: A survey on opportunities, applications, and future directions. arXiv preprint arXiv:2110.04160.
[23]. Vijayalakshmi, K., Sitharselvam, P. M., Thamarai, I., Ashok, J., Sathish, G., & Mayakannan, S. (2024). Secure and Private Federated Learning through Encrypted Parameter Aggregation. In Handbook on Federated Learning (pp. 80-105). CRC Press.
[24]. Hasan, M., Rahman, M. S., Janicke, H., & Sarker, I. H. (2024). Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis. arXiv preprint arXiv:2401.03530.
[25]. Ali, S., Li, Q., & Yousafzai, A. (2024). Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey. Ad Hoc Networks, 152, 103320.
[26]. Oualid, a., Maleh, y., & Moumoun, l. (2023) Federated learning techniques applied to credit risk management: a systematic literature.
[27]. Yang, F., Abedin, M. Z., & Hajek, P. (2023). An explainable federated learning and blockchain-based secure credit modeling method. European Journal of Operational Research.
[28]. Yu, F., Lin, H., Wang, X., Yassine, A., & Hossain, M. S. (2022). Blockchain-empowered secure federated learning system: Architecture and applications. Computer Communications, 196, 55-65.
[29]. Chatterjee, P., Das, D., & Rawat, D. B. (2023). Federated Learning Empowered Recommendation Model for Financial Consumer Services. IEEE Transactions on Consumer Electronics.
[30]. Xu, S., Liu, S., & He, G. (2021, October). A method of federated learning based on blockchain. In Proceedings of the 5th International Conference on Computer Science and Application Engineering (pp. 1-8).
[31]. Yang, F., Qiao, Y., Abedin, M. Z., & Huang, C. (2022). Privacy-preserved credit data sharing integrating blockchain and federated learning for industrial 4.0. IEEE Transactions on Industrial Informatics, 18(12), 8755-8764.
[32]. Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., & Yan, Q. (2020). A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network, 35(1), 234-241.
[33]. Oktian, Y. E., & Lee, S. G. (2023). Blockchain-Based Federated Learning System: A Survey on Design Choices. Sensors, 23(12), 5658.
[34]. Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022). Federated learning review: Fundamentals, enabling technologies, and future applications. Information processing & management, 59(6), 103061.
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