False Positives Masterclass Part 3: How to reduce FPs inside your system

Okay, so here is the thing about reducing false positives. Most teams want to jump straight into tactics. Tune the rule. Adjust the threshold. Add an exemption. Move the weird edge cases into manual review. Fine. All of that might be useful. But honestly, if that is where you start, you are probably guessing. And guessing in fraud prevention is not exactly my favorite operating model. Not because it never works. Sometimes it does. Which is almost worse, because then everyone gets confident. Not a good look. In this episode, I continue the False Positives Masterclass by moving from measurement and bucketing into the part everyone actually wants to get to: fixing the parts of the system that are misbehaving. But the point is not just to reduce false positives. The point is to reduce false positives without creating a new fraud problem you only discover three weeks later when the losses mature and everyone starts quietly looking at the dashboard like it personally betrayed them. This episode is about discipline. It is about manual review, fraud rules, fraud model precision, fraud model recall, shadow mode testing, data quality issues, and the uncomfortable but necessary question every fraud team eventually has to ask: is this rule actually helping, or have we just been emotionally attached to it since that one fraud spike in 2022? ## **What you’ll hear in this episode:** - Why reducing false positives should start with manual review, not instinct - How to decide whether a fraud rule should be removed, downgraded, or improved - Why fraud model precision and fraud model recall matter when rules catch fraud but hurt good users - How to build exclusions without accidentally creating a back door for fraudsters - Why shadow mode testing and challenger rules are essential before release - How data quality issues can make otherwise reasonable fraud prevention logic misbehave - Why fraud operations teams need to be pragmatic, not elegant, when the data is broken ## **You should listen to this episode if you:** - Own fraud rules, fraud detection rules, models, AI agents, or review flows - Are trying to reduce false positives without increasing fraud losses - Have a high false positive rate but are not sure which part of the system is causing it - Need a more structured way to review manual review samples and top offenders - Are dealing with corrupted data, noisy signals, or flows where fraud prevention logic keeps misfiring