Feedzai unveiled RiskFM (Risk Foundation Model) in March 2026, the industry's first Tabular Foundation Model purpose-built for financial data and risk decisioning. In October 2025, Feedzai was valued at more than $2 billion following an investment round of approximately $75 million. The company differentiates through proprietary AI research, industry-leading scale, and a comprehensive multi-product platform consolidating fraud detection, anti-money laundering, and risk operations.
Cyborg Score Rationale
Feedzai demonstrates exceptional execution across product innovation, market valuation, and customer impact. The company doubled outcomes to more than $2B in losses prevented and 20M+ analyst hours saved. Recent foundation model launch signals frontier AI leadership in a high-growth regulatory-driven vertical with sticky, mission-critical use cases.
Top Insights
Feedzai annually risk-assesses $9T in payments across 120B events worldwide spanning the entire financial risk lifecycle, providing unparalleled data advantage for training foundation models.
Feedzai launched Feedzai Orchestration and Feedzai IQ in 2025, empowering financial institutions to make more intelligent, rapid, and precise risk assessments.
Feedzai's technology protects 900 million people in 190 countries from fraud.
Feedzai introduced the TRUST Framework, embedding responsible design into every facet of GenAI development so systems remain fair, explainable, and secure.
Named Competitors
Kount — Fraud prevention and digital identity verification
Sift — Digital trust and fraud prevention platform
Actimize — AML and fraud detection for financial institutions
Stripe Radar — Real-time fraud and payment risk detection
Recent Developments
(March 2026) Launched RiskFM, industry's first Tabular Foundation Model for financial crime prevention covering fraud detection, AML, and risk decisioning
(October 2025) Closed $75M funding round at $2B valuation with new investors Lince Capital, Iberis Capital, and Explorer Investments
(June 2025) Launched Feedzai IQ, enabling network fraud intelligence through privacy-preserving federated learning across institutions
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