Cutting False Positives: How AI-Native Models Improve Fraud Detection for Fintechs
Discover how fintechs cut false positives, speed onboarding, and improve ROI with Coris’ AI-native fraud detection and merchant risk models.
Discover how fintechs cut false positives, speed onboarding, and improve ROI with Coris’ AI-native fraud detection and merchant risk models.
Fraud is rising, but false positives are the real profit killer. Rates of 95% or higher force teams to burn resources chasing “fraud” that isn’t fraud at all. The result: wasted labor, lost conversions, and frustrated customers.
The cost is massive:
For many institutions, investigating false positives isn’t optional — it’s a regulatory necessity. But treating them as “just the cost of doing business” destroys ROI.
Most fraud systems weren’t built for today’s fintech realities. Static rules, disconnected tools, and generic models can’t adapt fast enough. Instead of catching fraud, they bury analysts in false alarms.
The result:
Legacy tools don’t just miss fraud — they actively drain efficiency.
Coris was designed from the ground up for fintech and merchant risk. Unlike generic fraud tools, our platform combines:
This approach cuts false positives, streamlines reviews, and delivers consistent, explainable decisions.
Across our customer base, fintechs using Coris report:
Real ROI comes from fewer wasted investigations and higher conversion rates — not just fraud prevented.
Fraud defense shouldn’t be measured only by losses avoided. The bigger lever is operational efficiency:
The math is simple — cutting false positives pays for itself.
Fraud isn’t going away, but false positives don’t have to cripple ROI. With AI-native infrastructure, fintechs can:
It’s time to move past outdated tools. Fintech growth depends on fraud systems that reduce noise, not create it.