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AI-powered fraud prevention and risk management platform for online businesses.
Calculates real-time risk scores using global machine learning consortium models. Detects account takeover attempts by monitoring user behavior and device signals. Prevents payment fraud and chargebacks during online checkout transactions. Automates custom workflows and review queues to lower manual fraud monitoring.
Global data network feeds over 1 trillion annual event signals into predictive models. Machine learning automatically adapts to emerging fraud patterns without rigid manual rules. Real-time decisioning APIs deliver dynamic scores in milliseconds during critical user events. Comprehensive coverage across account creation, login, and checkout prevents diverse attack vectors.
Category: AI & Automation
Team Size: 100+
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Sift (formerly Sift Science) is an enterprise digital trust and safety platform that leverages real-time machine learning to detect and prevent online fraud. The system analyzes over one trillion annual network events across global digital properties to generate dynamic risk scores for user actions. By continuously processing behavioral patterns, device fingerprinting, and account interactions, Sift protects businesses against payment fraud, account takeovers, and fake user signups.
Founded in 2011 by Jason Tan and Brandon Ballinger in San Francisco, California, Sift was created to democratize machine-learning-driven fraud prevention. The founders recognized that smaller and mid-sized e-commerce platforms lacked the extensive engineering resources required to build complex risk engines. Sift abstracted this infrastructure into an accessible cloud platform that safeguards digital transactions.
