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15/08/2026 6:12 pm
TL;DR: VPNs now account for 15.3% of observed sessions, up nearly 40% year over year, according to Fingerprint’s 2024 Device Intelligence Report, showing how anonymized traffic is eroding the reliability of IP, location, and device signals in fraud decisioning. The governance problem is no longer whether to detect VPNs, but how to separate legitimate privacy from misuse without amplifying false positives.
NHIMG editorial — based on content published by Fingerprint: Best VPN Detection Tools for Fraud Prevention in 2025
By the numbers:
- 15.3% of observed sessions originated from a VPN connection, up nearly 40% from the previous year.
Questions worth separating out
Q: How should security teams handle VPN users without blocking legitimate access?
A: Security teams should use VPN detection as a contextual risk signal, not as an automatic deny rule.
Q: Why do VPNs create problems for fraud prevention and identity verification?
A: VPNs hide the user’s apparent origin, which weakens geolocation, IP reputation, and device correlation.
Q: How do organisations know if VPN detection is actually working?
A: Look for fewer blind spots without a spike in unnecessary reviews.
Practitioner guidance
- Correlate VPN detection with device and behavioural signals Feed anonymised-connection data into models that also use browser consistency, session velocity, and historical account behaviour so that one masked IP does not determine the outcome on its own.
- Differentiate privacy use from abuse with policy thresholds Define separate response paths for corporate VPNs, residential VPNs, Tor, and proxy traffic, then map each path to step-up verification, review, or allow decisions based on transaction risk.
- Retire static IP blocklists as a primary control Replace fixed deny lists with adaptive detection that uses ASN data, TLS fingerprints, and recent connection patterns, because VPN providers rotate infrastructure faster than static lists age.
What's in the full article
Fingerprint's full article covers the operational detail this post intentionally leaves for the source:
- Comparative notes on the VPN detection tools themselves, including deployment fit, pricing, and scale considerations.
- Per-tool capability breakdowns for VPN, proxy, Tor, and anonymised traffic classification.
- Operational buying criteria for fraud teams that need to balance detection accuracy, false positives, and compliance.
- Implementation-oriented guidance on where each tool fits in a fraud or risk workflow.
👉 Read Fingerprint’s full analysis of VPN detection tools for fraud prevention →
VPN detection for fraud prevention: what teams need to know?
Explore further
15/08/2026 6:19 pm
VPN detection is now an identity-verification control, not just a network filter. The article shows that once anonymised traffic becomes common, fraud teams can no longer treat IP and location as stable identity evidence. That shifts the control problem toward trust frameworks that combine device intelligence, behavioural history, and risk-based verification. Practitioners should treat VPN visibility as part of broader identity assurance, not a standalone perimeter rule.
A question worth separating out:
Q: What should regulated businesses do when masked traffic comes from prohibited jurisdictions?
A: They should tie anonymised-traffic rules to jurisdiction controls, then apply transaction-specific escalation rather than relying on a simple block. The right response depends on the regulatory regime, the type of transaction, and whether the traffic pattern is consistent with normal customer behaviour.
👉 Read our full editorial: VPN detection is becoming central to fraud verification
