🆚 Sift vs. ThreatMetrix

It draws on behavior, device, and identity signals and a global data network of over one trillion annual events to generate real-time risk scores. It claims to reduce chargebacks significantly, detect and block account takeover attempts using device fingerprinting and MFA, protect against spam, scams, and phishing in user-generated content through text clustering, and integrate with custom workflows and rules engines for more precise fraud operations.

It combines a global shared identity network, embedded machine learning, and behavioral biometrics to distinguish trusted users from fraudsters. In addition, it monitors signals from devices, locations, and past behavior to detect anomalies such as account takeover, payment fraud, or use of spoofed devices.