Architected a real-time graph neural network and LLM verification engine analyzing 80,000 transactions per second to block fraudulent wire transfers before funds settle.
Traditional rule-based fraud systems were failing against modern synthetic identity theft and coordinated multi-account draining rings, while frustrating legitimate customers with false declines.
We constructed an ultra-fast streaming pipeline using Rust and Apache Flink combined with vector graph embeddings that immediately detect anomalous fund flow clusters.
Sub-millisecond API response times with Go and Rust microservices.
The combination of sub-15ms latency and deep context understanding has cut our fraud losses to virtually zero.
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