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The World's First RiskOps Platform for Unified Financial Crime Prevention.

Feedzai is the premier RiskOps platform designed to safeguard the global economy by managing financial crime risk across the entire customer lifecycle. In 2026, Feedzai's architecture remains the industry benchmark for high-throughput, low-latency transaction monitoring, processing over $17 billion in value daily. The platform's core revolves around 'Pulse,' a streaming engine that handles massive data volumes with sub-50ms latency, and 'Genome,' a visual link analysis tool that leverages graph technology to uncover complex money laundering networks. By shifting from reactive fraud prevention to a proactive 'RiskOps' methodology, Feedzai allows financial institutions to unify data silos between AML and fraud departments (FRAML). Its technical edge lies in 'Fair AI,' which addresses algorithmic bias in real-time, and its AutoML capabilities that allow data scientists to build, deploy, and monitor models 10x faster than traditional methods. As of 2026, Feedzai has solidified its position in the market as the go-to solution for Tier 1 banks and fintech unicorns requiring a cloud-native, SOC2-compliant, and highly scalable risk engine that balances frictionless user experience with rigorous security protocols.
Feedzai is the premier RiskOps platform designed to safeguard the global economy by managing financial crime risk across the entire customer lifecycle.
Explore all tools that specialize in aml screening. This domain focus ensures Feedzai delivers optimized results for this specific requirement.
A proprietary stream-processing architecture that calculates thousands of features per transaction in milliseconds.
Graph-based technology that connects disparate data points to uncover complex fraud rings and money laundering paths.
An integrated fairness framework that detects and mitigates bias in machine learning models automatically.
Automated machine learning pipelines specifically optimized for imbalanced datasets typical in fraud detection.
Native ingestion of device and behavioral signals (typing cadence, mouse movement) to detect bot activity.
Allows banks to import their own models (Python, R, H2O) into the Feedzai ecosystem via PMML or API.
AI-driven alert prioritization that ranks cases by risk score and potential loss value.
Initial architecture review and scoping with Feedzai Solutions Architects.
Data mapping and schema definition for transaction and user entity objects.
Deployment of Feedzai Pulse engine within client VPC or Feedzai Cloud.
Historical data ingestion for baseline machine learning model training.
Configuration of API endpoints for real-time transaction streaming.
Integration with third-party data providers via Feedzai's library.
Custom rule development and ML model validation using AutoML.
User acceptance testing (UAT) in a sandbox environment.
Training of analysts on the Case Manager and Genome interfaces.
Production go-live with 'Shadow Mode' monitoring for initial 14 days.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform for its massive scalability and ability to combine fraud and AML data, though some note the implementation complexity for smaller institutions."
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