A streaming AI pipeline that scores every transaction for fraud in milliseconds — with full audit trails and strict latency budgets.
A fast-growing fintech needed to score every transaction for fraud in real time without adding perceptible friction at checkout. Their existing rules engine caught obvious abuse but missed sophisticated patterns, while batch ML models ran too slowly to block transactions before settlement. Regulators and partners demanded complete auditability — every decision explainable, every model input preserved, every override logged.
The fraud team wanted higher catch rates without increasing false positives that anger legitimate customers. Engineering needed a architecture that could evolve models weekly without downtime or compliance gaps.
Froxfire designed a streaming fraud-scoring pipeline that sits inline with the payment authorization path. Transaction events flow through a low-latency stream processor where ensemble models evaluate velocity, device fingerprint, merchant category, and behavioural signals in parallel. Decisions return in tens of milliseconds, well inside the checkout SLA.
Every score, feature vector, model version, and human override is written to an immutable audit log — giving compliance teams 100% coverage for examinations and dispute resolution. We built shadow-mode deployment so new models could be validated against live traffic before taking production traffic, and automated rollback if error rates spike.
Fraud detection improved substantially: the platform catches 28% more confirmed fraud compared to the legacy rules-only system, while keeping false-positive rates within agreed thresholds. Median decision time is 55 milliseconds, so shoppers never feel the security layer working. Audit coverage is complete — regulators and internal risk teams can reconstruct any decision from stored evidence.
Risk analysts now iterate models with confidence, and engineering ships updates on a weekly cadence without fear of silent regressions or compliance blind spots.
The platform combines event streaming, real-time AI inference, security-hardened infrastructure, and compliance-grade audit logging. Feature stores feed consistent signals to training and serving, model versioning ties each production decision to a reproducible artifact, and monitoring alerts fire on latency, drift, and catch-rate anomalies.
Let's build a pipeline that's fast, accurate, and fully auditable.