A Unified Platform for Real-Time Fraud Detection Using Distributed AI Systems

The exponential growth of digital transactions has increased the risk of financial fraud and has required traversing advanced mechanisms for real-time fraud detection. This research proposes a converging platform to avail distributed Artificial Intelligence (AI) systems in proactive fraud detection through heterogeneous environments. By combining cloud based architectures, edge intelligence with federated learning techniques, the platform allows for the scalability of privacy preserving monitoring of financial and IoT enabled transactions. The system uses machine learning-inspired algorithms that enable anomaly detection; making it easier to perform dynamic analysis of the data they receive in a stream to detect suspicious activities quickly. Additionally, distributed big data approach and digital twin are used for detecting accuracy improvement by capturing complex patterns of transaction in real-time. The proposed framework also targets insider threats, API vulnerabilities and encrypted traffic attacks to offer a comprehensive risk intelligence layer for financial and digital marketplaces. Preliminary evaluations show integration of distributed AI with real-time analytics improves the detection of industrial significantly, while maintaining the scalability (or penetration) and data security of the solution. This platform is a proof of concept on how it is possible to improve the confidence in digital transactions, and reduce financial losses in the increasingly interconnected environments.