Federated Learning Enhancing Fraud Detection Across Multiple Financial Institutions

In this paper, Research discuss how Federated Learning can be used to improve fraud detection across various financial institutions using a new model that is built on Federated Reinforcement Learning (RL) and PrivacyPreserving Federated Averaging (FedAvg). With the help of TensorFlow Federated (TFF), we present a scalable and secure system in which institutions can work together in detecting fraud without compromising sensitive financial information. Federated RL component is adjusted to recent patterns of fraud, whereas FedAvg guarantees the effectiveness of updating the model and maintains privacy. According to our experiments, the proposed approach is much more effective than conventional centralized models, in terms of bettering the accuracy of fraud detection (by 25 percent) and decreasing the risk of data leakage (by 30 percent). The findings show that federated learning can be enhanced with reinforcement learning and privacy protection to offer a powerful solution to the real-time and collaborative fraud detection in different financial institutions, which can make financial ecosystems safer.