A privacy preserving federated deep learning system for early financial fraud detection and prevention

Traditional rule-based and classical machine learning approaches have been rendered insufficient in terms of adaptability, scalability, and detection accuracy due to the significant challenge posed by the rapid growth of digital financial transactions and the increasing sophistication of fraudulent activities. Additionally, centralized data aggregation introduces critical privacy, security, and regulatory concerns, which restrict real-world deployment and cross-institutional collaborations. This study proposes FL-E $$F^2$$ DP, a privacy-preserving and robust framework that integrates deep learning and federated learning for the early detection and prevention of financial fraud, as a solution to these challenges. The proposed architecture utilizes convolutional neural networks to autonomously acquire hierarchical feature representations from high-dimensional transactional data, thereby facilitating the precise identification of intricate and evolving fraud patterns. Federated learning enables the collaborative training of models across distributed financial institutions without the exchange of raw transaction data, thereby guaranteeing data confidentiality, regulatory compliance, and improves system robustness. In addition, the framework integrates automated mitigation unified risk fusion, guideline-based control verification, and recommendation reporting to offer proactive fraud prevention and comprehensive risk assessment. The proposed approach consistently outperforms state-of-the-art methods, as evidenced by extensive experiments conducted on a widely used CCFD and PSFD datasets. This approach achieves superior accuracy, precision, recall, and F1-score. The effectiveness, scalability, and reliability of the proposed framework for real-world financial fraud detection in secure and distributed environments are validated by these results.