Neuromorphic graph-analytics engine detecting synthetic-identity fraud in real-time: Safeguarding national payment ecosystems and critical infrastructure

Adeshina, Yusuff Taofeek and During, Adegboyega Daniel (2025) Neuromorphic graph-analytics engine detecting synthetic-identity fraud in real-time: Safeguarding national payment ecosystems and critical infrastructure. World Journal of Advanced Research and Reviews, 27 (2). pp. 630-643. ISSN 2581-9615

The proliferation of synthetic identity fraud poses an unprecedented threat to the United States' financial infrastructure, with estimated annual losses exceeding $6 billion across payment ecosystems. This research presents a novel neuromorphic graph-analytics engine designed to detect synthetic identity fraud in real-time, leveraging advanced graph neural networks (GNNs) and transformer-based architectures to protect critical national payment systems. The proposed framework integrates heterogeneous temporal graph analysis with cloud-optimized streaming capabilities, achieving a 97.3% detection accuracy while maintaining sub-millisecond response times. Through comprehensive analysis of transaction networks and entity relationships, this system demonstrates superior performance in identifying sophisticated fraud patterns that traditional rule-based systems fail to detect.