Adaptive Behavioral Biometrics
Featurespace’s Adaptive Behavioral Biometrics uses behavioral data on websites and mobile apps to detect anomalous activity to stop new fraud attacks and suspicious money laundering activity, including specific attacks such as Malware, MITM, Account Takeover and Phishing Attacks, all in real time using machine learning.
Adaptive Behavioral Biometrics provides an additional tool for fraud analysts, enabling them to visualise and compare a user's behavior in different sessions, spot anomalies and identify cases of account takeover, chargeback fraud, and bot and malware detection.
The system tracks a broad range of features including the way a user types on a keyboard, mouse movements or tap behavior, the time spent between text fields, and system features such as device, browser, operating system and timezone to produce a 'session fingerprint' and build a profile of 'normal' activity for each user.
Crucially these profiles are so accurate that phishing and malware activity can be spotted even if the session is generated by a genuine customer.
Cross-platform
No reliance on JavaScript
99% Proven accuracy
Key Features
Self-learns from customer behavior adapting in real time
Automatically detects and stops new fraud types
Easy to deploy using proven, scalable technology
Non-intrusive technology
A sample abnormal session, as presented in the ARIC UI
Featurespace by numbers
events processed this year
increase in high-ticket fraud detection
new fraudulent transactions blocked
reduction in genuine transactions declined
customers protected
Multi-Award Winning Fraud Prevention
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