Sub-50ms Financial Fraud Detection Model

High-throughput time-series anomaly detection scoring credit card transactions against 120 graph feature vectors.

Intelligence Gap / The Problem

Legacy rule-based fraud engines typically take 100-250ms to score a transaction, which is too slow to sit inline in a real-time authorization path without adding noticeable checkout latency. Rule-based systems are also brittle against fraud patterns that don’t match a predefined rule.

Solution Architecture / Internal Flow

We replaced the rule engine with a streaming architecture. Transaction events are ingested from Kafka, hydrated against an in-memory feature store holding 120 graph-based user metrics, and scored by an ONNX-optimized PyTorch model running on edge workers close to the request origin. The entire path — ingest, feature hydration, inference, decision — is designed to avoid a database round-trip on the hot path.

Kafka event stream → in-memory feature store (120 graph metrics, ~4ms hydration)
  → ONNX model inference
  → risk decision (approve / deny / manual review)
  → async write-back to persistent store (off hot path)

Performance Matrix / Evaluation

Metric	Falconic Protocol	Baseline
Inference Latency	28ms	120ms
False Positive Rate	0.4%	2.8%
Throughput	50k TPS	10k TPS

Entropy Audit / Failure Analysis

An early version of the feature store used synchronous reads from the primary database as a cache-miss fallback, which occasionally spiked latency to 200ms+ under load and defeated the point of the sub-50ms target. We moved to a fully async write-back model with a strict in-memory-only read path, accepting a small window of feature staleness in exchange for consistent latency.

Communication Layer / Discussion

Peer Protocol Interface / Discus Integration Pending