VPN IP Risk Detection API
VPN IP risk detection API flags sessions with properties that don’t match a user’s normal sign-in behavior. This can include sign-in locations, application characteristics, device and browser details, time zone and currency, and country metadata. This detection type has historically incurred more noise than other fraud detections, and is best used in conjunction with other risk assessment methods to provide additional context.
Identifying connections routed through VPNs, proxies, and Tor exit nodes provides essential context for fraud, traffic-quality, and ad-tech risk workflows. By integrating proxy & VPN detection into your systems, you can stop cyber criminals while also protecting your real customers and employees.
The main challenge of VPNs is identity obfuscation, which makes it difficult to connect online activity with a physical location or identity. This impacts digital security, geo-targeting, and compliance measures by distorting IP-dependent decisions.
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However, detecting VPN activity isn’t a guarantee of fraud or abuse. Remote employees, global travelers, and privacy-conscious users all generate anonymized signals that can look suspicious without deeper context. Blocking these sessions could create friction, trigger false positives, and strain review capacity.
To avoid these challenges, fraud and security teams need to integrate VPN detection into their layered intelligence tools. Solutions that combine VPN identification with user history, device data, and behavioral analytics offer the most comprehensive context. This enables teams to identify potential risks and prioritize them accordingly. Fingerprint’s Smart Signals platform is one of these tools, combining VPN detection with more than 100 device, network, and browser signals to spot suspicious clusters and reduce the risk of false positives.