How to Reduce False Positives Without Letting Fraud Slip Through

Fraud detection teams walk a constant tightrope: block too much and lose good users. Block too little, and let fraud run wild.

False positives don’t just frustrate users — they hurt conversion rates, create unnecessary support costs, and ultimately damage brand trust. But many systems are still using rigid, binary rules — especially for IP reputation.

The Hidden Cost of False Positives

Every time you flag a legit user as suspicious because of a VPN, cloud host, or proxy signal, you’re trading safety for friction. And the truth is, most blocklists aren’t smart enough to tell the difference.

• 🛑 Users get blocked for using privacy tools
• 📉 Conversion rates drop
• 🎟️ Support queues get flooded with “why was I flagged?”

And yet, if you don’t flag risk at all, abuse flows in — from trial fraud to card testing, scraping, and more.

A Better Way: Context-Aware IP Scoring

CandycornDB approaches this differently. We don’t just flag VPNs. We score IPs using:

• 📊 Subnet behavior and relationships
• 🌐 ASN reputation and history
• 🔍 Proxy detection layered with port scanning and usage patterns

That means a mobile user on a VPN in a clean subnet might get a 15. But a scraper rotating through shady hosts in a known abuse ASN might get an 88 — even if both are “VPNs.”

Smarter Decisions, Fewer Headaches

With a real-time risk score instead of a binary label, your system can:

• ✅ Let low-risk VPN traffic through (and win more legit users)
• ✅ Block coordinated subnet abuse before it escalates
• ✅ Feed high-risk scores into your fraud pipeline dynamically

See It in Action

Want to see how it compares? We’ve helped platforms reduce IP-based false positives by up to 40% — while catching Tor nodes and anonymized traffic that traditional blocklists miss.

Try CandycornDB free or view our docs to see how we help fraud teams score smarter — and stop blocking good users.