First-party fraud is different from synthetic identity fraud in one important way: the person is real. They open the account legitimately, build trust over weeks, then dispute valid charges or intentionally default. The challenge is catching the intent before approval, not after the loss.

The Booking.com fraud team described this problem directly. Andreas Zodhiates, their Head of Payment Fraud, noted that device signals "act as fraud detector and a trust enabler." The same persistent device ID that flags a fraudster also allows legitimate returning customers to move through with less friction. That is the dual outcome device intelligence provides.

The key difference between Fingerprint and the browser cookie your existing stack may already rely on is persistence. A 7-day cookie cannot tell you whether the person logging in today accessed a different account on the same device last month. Fingerprint's visitor ID persists for months, across cookie clears, browser resets, and VPN changes. That continuity surfaces account takeover attempts, credential-stuffed sessions, and repeat first-party fraud patterns that cookies structurally cannot detect.

Over 200 fintechs have deployed Fingerprint as part of their fraud stack, with 99.5% device identification accuracy across browsers and device types. The integration takes an afternoon. The signal pipes directly into your existing risk engine via webhook or REST API, with no infrastructure changes required.

If your team is working on reducing first-party fraud or account takeover losses before the approval decision, we are happy to walk through how the device layer fits into your current model.

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