Ask any fraud analyst what slows them down, and the answer is rarely the fraud itself — it's the noise. A detection system tuned for maximum sensitivity flags everything that looks slightly unusual, and a team spends its day clearing alerts that were never fraud to begin with.
The reflex is to loosen the rules. That's the wrong lever — it trades a workload problem for a loss problem. The right lever is precision: surfacing fewer alerts, each far more likely to be genuine.
Score, don't gate
Binary rules — flag or don't — throw away information. A model that outputs a calibrated risk score lets you route cases by confidence: auto-clear the clearly-benign, auto-hold the clearly-fraudulent, and send only the genuinely ambiguous middle to a human.
If more than four in five of your analysts' reviewed alerts turn out clean, you don't have a fraud problem — you have a threshold problem.
Tune to the cost, not the count
The right threshold isn't where alert volume feels comfortable — it's where the expected cost of a missed fraud equals the expected cost of a wasted review. Make that trade-off explicit and revisit it as conditions change.
Fewer, better alerts don't just save hours — they rebuild the analyst's trust that a flag means something.
Espero Research
The Espero AI team builds and deploys production-grade credit, fraud, and customer-intelligence systems for financial institutions across East Africa — with explainability and governance built in from the first line.