Market map / AI risk engines
Sift
Machine-learning risk scoring across the customer journey for fraud and abuse.
- Category
- Digital trust & safety (ML risk)
- Lane
- AI risk engines
- Founded
- 2011
- Ownership
- Private; VC-backed (~$1B+ valuation; Insight)
- HQ
- San Francisco, US
Summary
A digital trust and safety platform using machine learning and a global data network to score fraud and abuse risk in real time across payments, accounts, and content.
Best for
Consumer platforms wanting ML fraud and abuse risk scoring across many use cases.
Consider if
You need behavioral biometrics or bank-grade AML rather than broad ML risk.
Strengths
- ML risk scoring on a large data network
- Covers payments, account takeover, content abuse
- Real-time decisioning
Considerations
- Broad risk focus over specialized AML
- Tuning needed per use case