Fraud Detection

Fraud is a moving target by its nature. The patterns that worked last year are mostly caught now, and the ones that are working now will look obvious in another twelve months. The systems that hold up over time are the ones that keep learning, keep tuning the false-positive rate, and keep the human review loop tight.

What we deploy

• Real-time scoring on transactions, signups, or events. The latency budget is low enough that the scoring fits inside your authorization flow.
• Anomaly detection on patterns your rules engine misses. The system catches behaviors that don't match a known signature but also don't look like a normal customer.
• Network analysis. Linking accounts, devices, and behaviors that look unrelated until you actually map the graph between them.
• Case management for the review team. The flagged events arrive in a queue with the evidence attached, in priority order.

The unglamorous parts that matter

Calibrating the false-positive rate is most of the actual work. A fraud system that blocks too aggressively costs you legitimate customers, and one that blocks too softly costs you write-offs. We tune the system to your specific tolerance, monitor the drift over time, and adjust on a regular cadence.

What we won't pretend

That AI catches everything. The good systems handle the bulk of obvious fraud and surface the rest for human review. The fraud teams that succeed tend to treat AI as their first line of defense, rather than as their only line. Get in touch with your specific risk profile.