Catching fraud that was designed to blend into real traffic

Fraudulent online activity is increasingly sophisticated, with fraudsters using anti-detect browsers to create synthetic identities and evade traditional detection methods by manipulating browser fingerprints and device characteristics. In response, Fingerprint has enhanced its device intelligence capabilities, introducing a deeper layer of browser-level analysis and a new machine learning layer that detects both known and evolving manipulation patterns. This update has significantly improved the detection of anti-detect browser activity, increasing coverage from 2.1% to 6.4%, and overall tampering detection from 6.5% to 7.8%. These improvements provide more reliable signals, enabling teams to distinguish between legitimate users and manipulated environments, and make informed decisions with reduced noise and fewer false positives. Fingerprint’s ongoing efforts aim to adapt to evolving fraud techniques by continuously training machine-learning models on new manipulation patterns, thus helping users regain trust in their traffic.

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