Adaptive secure federated cloud framework for high-accuracy fraud detection in financial systems

Cloud computing is revolutionizing the finance sector for scalable machine learning applications for use in fraud detection, risk assessment, and credit scoring, among others. However, still, there is still a need for balancing high accuracy with strict adherence to data privacy and regulatory compliance. This paper discusses a new framework called the Secure Federated Cloud for Financial Analytics, dubbed SFC-FA, which is specifically constructed for ML-driven fraud detection in decentralized financial systems. We propose a novel learning paradigm, Adaptive Secure Federated Learning (ASFL) – an advanced federated approach that integrates reinforcement learning (RL), differential privacy (DP), and homomorphic encryption (HE) for privacy-preserving and resource-efficient fraud detection in distributed financial environments. By utilizing real-time workload patterns, ASFL ensures efficient resource allocation with minimal latency and high computational performance. Experimental evaluation of the federated financial transaction dataset establishes that the proposed framework correctly detects fraud with an accuracy value of 97.5%, which is 9% more accurate than traditional federated ML models and reduces resource consumption by 12%. This work provides a transformational solution in terms of the deployment of robust, privacy-preserving ML applications in the financial sector, providing secure, accurate, and scalable fraud detection on the cloud.