Fraud & risk analytics · Feedzai

Feedzai

Enterprise ML risk-scoring platform (RiskOps) that combines behavioral/device intelligence with network analysis for banks.

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Feedzai is an enterprise fraud and financial-crime platform for banks, card issuers and payment processors, sold as a unified 'RiskOps' system spanning onboarding, payment fraud and AML transaction monitoring. Its core signal blend is real-time behavioral and device intelligence layered with network intelligence ('Feedzai IQ'), which traces relationships between accounts and transactions to surface coordinated fraud rather than scoring events in isolation. Proprietary machine learning models generate risk scores and explanations for investigators, and the platform is used by central banks and card networks for large-scale monitoring. It is delivered as enterprise software integrated into a bank's transaction infrastructure rather than a self-serve API. Compared with Featurespace's per-customer behavioral baselining or NICE Actimize's compliance-heavy case management, Feedzai emphasizes network/relationship intelligence alongside behavioral scoring.

At a glance

Vendor Feedzai
Pricing model Quote only
Free tier —
Deployment Cloud, Self-hosted
Open source No
Best for Large banks and payment processors that want network/relationship-based fraud detection alongside behavioral scoring.

Pricing

Enterprise pricing customized to institution size and transaction volume; no self-serve signup or published rates.

Pricing has not been verified yet — see the vendor's site.

Features

  • Real-time behavioral and device intelligence scoring
  • Feedzai IQ network intelligence across transaction relationships
  • Machine learning risk models with investigator-facing explanations
  • Payment fraud detection across banking rails
  • AML transaction monitoring and watchlist screening
  • Automated onboarding/account-opening orchestration
  • Case management for the full financial-crime lifecycle
  • High-throughput processing for tier-1 bank volumes

Profile last reviewed September 21, 2026

Head to head

Alternatives

Featurespace Enterprise ARIC engine that models each customer's real-time behavior to flag payment fraud and financial crime for banks. NICE Actimize Enterprise AML transaction-monitoring and fraud suite with heavy case-management and regulatory-filing workflows for banks. Unit21 No-code rules and case-management platform that lets fraud/AML ops teams build detection logic and investigate without engineers.

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Terms to know

Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning. Transaction monitoring Ongoing, largely automated screening of financial transactions to flag activity that may indicate fraud or laundering.

Related guides

How to choose a fraud detection tool Fraud platforms split by primary signal — biometrics, device data, consortium networks, compliance case management — and by who they're built for.