Multi-cloud data platforms for real-time fraud detection and prevention

In today’s fast-paced digital world, fraud detection stands out as a key area of both academic interest and real-world development—particularly as businesses increasingly depend on multi-cloud setups. This review explores how AI helps power those real-time defenses. It unpacks the core architectural elements, AI and machine learning approaches, and real-world metrics drawn from academic literature. A theoretical model is proposed that supports scale and privacy compliance, using stream processing and distributed learning. Experiments show that tools like XG Boost, LSTM, and Federated Learning work well in live, multi-cloud setups. The review also points to important research gaps and lays out possible next steps to improve fraud detection’s flexibility, ethical grounding, and long-term resilience across cloud systems.