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Feedzai RiskOps
Unified platform for fraud detection, AML, and identity verification

Feedzai RiskOps
Unified platform for fraud detection, AML, and identity verification
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Feedzai RiskOps Description
Feedzai RiskOps is a cloud-based platform that provides financial crime risk management across fraud detection, anti-money laundering (AML), and identity verification. The platform consolidates risk signals from multiple sources including demographic information, watchlist screening, behavioral biometrics, device intelligence, account changes, and transaction history to create a comprehensive view of customer risk. The system uses machine learning and behavioral analytics to detect fraud and financial crime in real-time across multiple channels. It processes transaction data at scale, analyzing over 3000 payments per second and protecting more than $6 billion in transactions annually. Key capabilities include account opening fraud detection, new account fraud prevention, account monitoring, transaction fraud detection, scam prevention, risk management for acquirers and payment service providers, KYC/CDD processes, watchlist screening, AML transaction monitoring, and SAR filing automation. The platform features a unified case management system that enables collaboration across teams managing different aspects of financial crime risk. It includes dashboards and reporting tools for performance monitoring and threat identification. The system incorporates AI governance features designed to ensure fairness, explainability, and regulatory compliance. RiskOps operates as a single platform rather than separate tools for different risk functions, providing consistent workflows and user experience across identity, fraud, and AML use cases.
Feedzai RiskOps FAQ
Common questions about Feedzai RiskOps including features, pricing, alternatives, and user reviews.
Feedzai RiskOps is Unified platform for fraud detection, AML, and identity verification developed by Feedzai. It is a GRC solution designed to help security teams with Anti Fraud, Fraud Detection, Machine Learning.
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