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#fraud-prevention

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Score Zero Has Two Meanings: Branch on evidence_state First

If your antifraud rule treats every risk.score == 0 as "clean," you will misread two very different situations the same way. On IP99 by ThreatHunter, score zero can mean never seen (evidence_state: no

5 min readip99.hashnode.dev

Marc NewsteadOct 26

Building Systems That Won't Be Fooled: A Developer's Guide to AI-Orchestrated Deception

The Problem We're Not Architecting For We've spent years hardening our systems against SQL injection, XSS, and CSRF. We've built robust authentication flows, implemented rate limiting, and deployed WA

4 min readicentric-ai-and-automation.hashnode.dev

Marc NewsteadSep 28

Your Bot Detection Is Breaking Agentic Commerce (And You Won't Know Until It's Too Late)

The Problem Nobody's Monitoring If you're running fraud detection on an e-commerce platform, you've probably tuned your bot management rules to block credential stuffing, inventory hoarding, and scrap

5 min readicentric-ai-and-automation.hashnode.dev

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KanikaJul 21

The Digital Identity You Trust Most Could Be a Fake-Until Deepfake Detection KYC Verifies Its Authenticity

Deepfake detection KYC adds a security layer to identity verification by identifying manipulated media, preventing fraud, and improving digital trust.

7 min readai-threat-intelligence.hashnode.dev

James KYC researcherJul 14

Passive Liveness Detection in 2026: OZ Liveness vs FaceTec vs iDenfy – A Developer's Comparison

A developer's comparison of three liveness SDKs — architecture, injection attack protection, platform support, and pricing at real volume.

12 min readprimebiometry.hashnode.dev

Onkar MayekarJul 1

The Ambulance That Cried Wolf.

SMS built thirty years of urgency into a single notification sound. Brands are spending it on flash sales. The fraud alert arrives into the silence that follows.

10 min readhetvabhas.com

VerafyeMay 25

3 signs you might be a high target for fraudsters

Introduction:

6 min readverafye.hashnode.dev

Edith HerouxMay 4

Fraud Prevention Automation Case Study: 65% False Positive Reduction

When I joined the fraud operations team at a mid-sized retail bank processing 15 million transactions monthly, we were drowning. Our transaction monitoring system generated 12,000 alerts weekly, requiring manual analyst review for each one. With a te...

7 min readtechnicalaime.hashnode.dev

Jamie DornanMay 4

Implementing Fraud Prevention Automation: A Practical Guide

Transforming fraud operations through automation can feel overwhelming, especially when you're starting from a position where analysts manually review every alert and case management happens in spreadsheets. Having led multiple implementations across...

6 min readtechworldai.hashnode.dev

Cheryl D MahaffeyMay 4

Understanding Fraud Prevention Automation in Modern Retail Banking

The retail banking landscape faces an unprecedented surge in sophisticated fraud schemes, from synthetic identity fraud to account takeover attempts that bypass traditional security controls. As institutions like JPMorgan Chase and Bank of America pr...

5 min readtechnoai.hashnode.dev

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