Cookie Consent
By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
Most fraud prevention platforms lean heavily on device fingerprinting and bot detection to stop fake account creation. And for low-effort fraud or basic automation, that works—sort of. But for cybercriminals with a bit more persistence or skill? It’s definitely not enough.
Device fingerprinting identifies things like IP address, browser type, OS, and screen resolution to uniquely tag a device. Bot detection analyzes interaction patterns to spot automation. These techniques are fast, widely adopted, and often built into onboarding workflows.
But modern fraudsters have already adapted to this:
The result: fake accounts still get through. The device fingerprints look new, and the bots behave more human than ever. You’re left chasing shadows, with little to no visibility into who’s actually behind the activity.
Stopping fake accounts requires a broader view—one that looks at relationships and patterns across accounts, not just individual signals.
Graph-based analysis enables you to:
When fingerprinting is used as just one node in a larger web of identity signals—paired with IP reputation, behavioral telemetry, sign-up velocity, reuse patterns, and session analytics—you get a much clearer picture of intent.
Bad actors don’t just try once. They test, iterate, and come back through different attack vectors. Fighting them requires defenses that aren’t tied to just one device or moment in time. It takes observability across identity graphs.
That’s why the future of fake account prevention isn’t fingerprinting alone. It’s a solution such as Verosint’s that offers fingerprinting plus graphing, correlated risk signals, and shared intelligence across your entire identity surface.
.png)