---
title: A practitioner's guide to reducing false positives
source_page: https://www.sardine.ai/whitepapers/practitioners-guide-reducing-false-positives-2026
canonical: https://www.sardine.ai/whitepapers/practitioners-guide-reducing-false-positives-2026
format: text/markdown
date: 2026-08-07T19:40:00.000Z
author: Chen Zamir
description: Blocked good customers rarely show up in your metrics. Get a four-part framework for measuring, diagnosing, and fixing false positives.
---

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---

# A practitioner's guide to reducing false positives

**Author:** Chen Zamir · **Published:** 2026-08-07T19:40:00.000Z · **Categories:** Fraud

Blocked good customers rarely show up in your metrics. Get a four-part framework for measuring, diagnosing, and fixing false positives.

When a fraud system blocks a legitimate customer, the signal goes dark: the complaint dies in a support ticket and never becomes a labeled event that feeds a model or a dashboard. False positives are hidden by design, which is exactly what makes them so hard to fix. 
This guide gives fraud leaders and operations teams a four-part working framework for surfacing them, tracing them to root cause, remediating the specific components responsible, and building an override layer above the entire stack. Inside, you'll find:
Five triangulation methods for measuring what your system hides, from rule simulation to 1% control groups
A seven-step root cause process that attributes every false positive to a specific rule, model, reviewer, or upstream partner
The mirror image method for building exclusions that release good users without releasing fraud
Four families of override heuristics strong enough to reverse an automated decline, from known good users to geographic chains
A shadow-to-ramp deployment plan for rolling out override logic without opening the door to fraud
Download now and get all the insights.