Building a Real-Time Fraud Detection Pipeline with Agentic AI and Streaming Feature Stores
I’m deliberately skipping the fraud labeling feedback loop here—it’s critical but out of scope for this post. In production I use a separate Kafka topic (`fraud_labels`) fed by customer disputes and manual reviews.
Flink’s state is checkpointed to S3 every 30 seconds; if a pod crashes, recovery is instant. This eliminates data loss during deployments.
I use sentence-transformers (`all-MiniLM-L6-v2`) to embed rule descriptions into vectors. This lets me cluster similar fraud narratives without manual tagging.
Latency is measured with `perf_counter` probes inside each pod. Cost is AWS pricing (m5.large, 1 Kafka broker, Redis cache.r5.large). Your mileage will vary.
In production, the agent reduces false positives by 18% over static rules, while maintaining the same fraud catch rate (92%).