FinTech Intelligence Platform
Real-time fraud capture at scale
A streaming fraud-intelligence engine scoring millions of transactions per minute.
Where the old way was failing
The bank's rules-based fraud engine caught patterns it already knew, while novel fraud rings adapted within days. Losses were growing 20% year over year.
Batch scoring meant fraudulent transactions often cleared before they were ever evaluated — detection that arrives after settlement is just reporting.
False positives were freezing legitimate customer payments, generating support load and churn among the bank's highest-value users.
What we built
Streaming feature platform
We built a real-time feature store computing behavioral, device, network, and velocity signals on the live transaction stream — every score uses data that is milliseconds old, not hours.
Ensemble scoring under 40ms
Gradient-boosted models, graph-based ring detection, and anomaly detectors vote on every transaction inside the authorization window, so fraud is blocked before money moves.
Adaptive learning loop
Analyst decisions feed straight back into training data. The system retrains on a rolling window, tracking concept drift so new fraud patterns degrade the model for days, not quarters.
What changed for the business
Fraud losses fell 58% in the first six months of production.
End-to-end scoring latency held under 40ms at 5M transactions per minute.
False-positive rate dropped 44%, cutting wrongly-frozen payments nearly in half.
New fraud patterns are now neutralized in days through the adaptive retraining loop.
“We went from finding fraud in yesterday's reports to blocking it inside the authorization window. It changed the economics of our risk team.”
Want results like these?
Book a strategy session and we'll map the same AI Engineering Framework onto your product, your data, and your industry.