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Feedzai

Bank and payment RiskOps platform

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Feedzai combines transaction fraud, scams, digital trust, AML transaction monitoring, and risk operations. Digital Trust adds behavioral and device context. Genome supports visual investigations. The evaluation should connect payment decision speed to the quality of the later investigation and model feedback.

Our assessment

Broad fraud and AML coverage. Merchant verification, underwriting, and identity capture require a detailed scope check.

Best fit and limitations

A strong candidate for banks, issuers, payment providers, and acquirers with high-value real-time fraud use cases. The strongest test includes both customer-session signals and payment outcomes.

  • Continuous digital authentication is not the same as initial legal identity verification.
  • Do not assume every RiskOps module is included in one contract.
  • Screening uses Neterium and external data feeds. Confirm each license, regional reporting, and current Genome packaging.

Tools 6

RiskOps platformShared risk operations
+
  • Connect data, analytical models, and operational controls
  • Fraud, identity, and AML product families
Source 1
Transaction fraud + scam preventionPayment risk
+
  • Risk decisions across payment activity
  • Customer behavior and transaction context
Source 1
Digital TrustDevice and behavioral intelligence
+
  • Behavioral biometrics and device analysis
  • Signals for malware and remote-access threats
Source 2
AML Transaction MonitoringFinancial crime monitoring
+
  • Rules and behavioral profiles with ML prioritization
  • Investigation and suspicious activity reporting support
Source 3
Watchlist ScreeningConnected screening
+
  • Neterium matching API with licensed sanctions, PEP, and media data
  • Matching alerts enter Feedzai Case Manager
Source 6
GenomeVisual link analysis
+
  • Explore relationships between entities and transactions
  • Investigate connected fraud and money-laundering patterns
Source 4

AI capabilities

Predictive models + explainable decisions

Feedzai emphasizes transaction and behavior models with explanations for analysts. Its Whitebox Explanations feature exposes factors behind decisions.

Score payment riskDetect behavior shiftsPrioritize AML alerts
What to validate

Measure fraud loss and approval rates together. Validate model drift, reason codes, and controls for feedback from late chargebacks.

Source 5

Implementation

Integration checklist
  • Instrument relevant digital sessions and connect payment decisions to outcome labels.
  • Test p95/p99 latency, degraded-mode behavior, and transaction replay.
  • Map payment events, account history, dispute outcomes, and review decisions to stable IDs.
Commercial scope

Quote required. Ask for pricing by decision volume, digital intelligence, AML, screening, analytics, and implementation.

Estimate total cost
Questions for the demo
  1. What incremental lift comes from device signals over our transaction-only baseline?
  2. How are scam victims distinguished from account-takeover attackers?
  3. Can we backtest a rule or model without affecting live decisions?

Sources 6

RiskOps platformwww.feedzai.com
Product
Digital Trustwww.feedzai.com
Product
AML Transaction Monitoringwww.feedzai.com
Product
Genome visual investigationswww.feedzai.com
Announcement
AI and model explanationswww.feedzai.com
Product
Watchlist screening engine and data providerswww.feedzai.com
Product

Reviewed September 17, 2026. Product claims come from public sources. Fit, limits, and evaluation questions are our analysis. This is not a hands-on performance test. Methodology