Feedzai

Bank and payment RiskOps platform

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 7

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

RiskOps StudioUnified risk configuration and investigations

Configure strategies, review investigations, and monitor operations in one workspace
Use documentation assistance and proactive investigation guidance
Inspect score contributors and threshold trends

Source 8

AI capabilities

Predictive models + RiskOps Studio guidance

Feedzai combines transaction and behavioral models with RiskOps Studio assistance for documentation, strategies, and investigations. Public guidance features do not establish autonomous case clearance or policy publication.

Score transaction riskPrioritize AML alertsExplain documentation and investigation context

What to validate

Validate model performance separately from generated guidance. Confirm human approval and permissions before using proposed changes.

Source 8

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.

Questions for the demo

What incremental lift comes from device signals over our transaction-only baseline?
How are scam victims distinguished from account-takeover attackers?
Can we backtest a rule or model without affecting live decisions?

Engineering

Watchlist API and data-provider scopeChecked 2026-09-18 RiskOps Studio setup and schema validationChecked 2026-09-18 RiskOps decision data boundaryChecked 2026-09-18

Your systemInputs & context

FeedzaiChecks & signals

Your controlsDecision & review

Illustrative integration boundary. Confirm the actual interfaces and decision authority.

Inputs: Customer records, payment parties, and client-managed screening lists · Customer data structures and configured risk rules · Customer, transaction, device, behavior, and selected third-party signals
Outputs: Screening matches in Feedzai Case Manager and screening audit records · Mapped and validated schemas, risk strategies, and investigation context · Risk profiles, configured rule/model outcomes, and investigation context
Webhooks: Not verified in public documentation.
Decision timing: Customer and transaction screening are described as real-time. Periodic list checks and data-provider SLAs remain separate constraints.
Deployment: Deployment options not verified.
Dependencies: Configured screening frequency and licensed risk-data feeds · SSO, IP allowlisting, and API credential setup · Self-service schema mapping and tests before use · Selected Feedzai products and data connections · Backtesting and challenger testing for policy/model changes

Verified details 15

Inputs
Customer records, payment parties, and client-managed screening lists
Source Checked 2026-09-18
Outputs
Screening matches in Feedzai Case Manager and screening audit records
Source Checked 2026-09-18
Decision timing
Customer and transaction screening are described as real-time. Periodic list checks and data-provider SLAs remain separate constraints.
Source Checked 2026-09-18
Connected provider
Neterium
Source Checked 2026-09-18
Connected provider
Acuris
Source Checked 2026-09-18
Connected provider
LSEG World-Check
Source Checked 2026-09-18
Dependency
Configured screening frequency and licensed risk-data feeds
Source Checked 2026-09-18
Inputs
Customer data structures and configured risk rules
Source Checked 2026-09-18
Outputs
Mapped and validated schemas, risk strategies, and investigation context
Source Checked 2026-09-18
Dependency
SSO, IP allowlisting, and API credential setup
Source Checked 2026-09-18
Dependency
Self-service schema mapping and tests before use
Source Checked 2026-09-18
Inputs
Customer, transaction, device, behavior, and selected third-party signals
Source Checked 2026-09-18
Outputs
Risk profiles, configured rule/model outcomes, and investigation context
Source Checked 2026-09-18
Dependency
Selected Feedzai products and data connections
Source Checked 2026-09-18
Dependency
Backtesting and challenger testing for policy/model changes
Source Checked 2026-09-18

Illustrative contract

An example for your internal adapter. This is not a vendor endpoint, request, or response.

json

{
  "contract": "internal-risk-review/v1",
  "vendorId": "feedzai",
  "internalCorrelationId": "example-001",
  "vendorRecordId": null,
  "control": "Shared risk operations",
  "evidence": [],
  "status": "pending",
  "decision": "review",
  "requiresHumanReview": true
}

Integration limits

Illustrative internal integration contract. This is not a vendor request, response, endpoint, or SDK example.
Empty country, deployment, or provider lists mean not verified in this review. A documented country refers to the specific product noted in its source, not universal platform coverage.
Provider scope varies between customer and transaction screening. This reference does not document a general Feedzai webhook protocol or establish all providers as included in a license.
The August 20, 2026 product article describes setup controls, not a public endpoint specification.
The platform combines multiple products. Published detection percentages were not treated as comparable accuracy measurements.

Sources 8

RiskOps platformwww.feedzai.com2026-09-18 · Reviewed

Product

Digital Trustwww.feedzai.com2026-09-18 · Reviewed

Product

AML Transaction Monitoringwww.feedzai.com2026-09-18 · Reviewed

Product

Genome visual investigationswww.feedzai.com2026-09-18 · Reviewed

Announcement

AI and model explanationswww.feedzai.com2026-09-18 · Reviewed

Product

Watchlist screening engine and data providerswww.feedzai.com2026-09-18 · Reviewed

Product

RiskOps Studio setup and data mappingwww.feedzai.com2026-09-18 · Reviewed

Product

RiskOps Studio product scopewww.feedzai.com2026-09-18 · Reviewed

Product

Source review dates are shown above. Product claims come from public sources. Fit, limits, and evaluation questions are our analysis. This is not a hands-on performance test. Methodology · Changelog