Convergence · Use Case

Fraud teams and AML teams
are catching different pieces of the same crime

Verafye brings both signal sets into a shared investigation surface — so neither team works a partial picture, and the full scope of the risk is visible before a decision is made.

The Problem

83% of financial institutions still run fraud and AML as separate teams

Separate teams mean separate data, separate queues, and separate decisions on what is often the same underlying criminal activity. The result: investigations that are closed without seeing the full picture, and SAR filings that tell half the story.

Fraud signals

  • Device fingerprinting
  • Behavioral biometrics
  • Account takeover patterns
  • Payment fraud indicators
  • Synthetic identity markers

Verafye

AML signals

  • Transaction typology matches
  • Entity relationship graph
  • Sanctions & PEP hits
  • Structuring detection
  • Cross-border flow patterns

Where Silos Fail

The gaps that separate tools create

Same mule account, two separate alerts

Fraud closes the account-level case. AML never sees the network. The ring continues operating through connected accounts neither team examined.

Siloed data, duplicated review effort

Fraud analysts query one system. AML analysts query another. Both spend time assembling context that already exists — in different databases.

No shared handoff between teams

A fraud escalation with AML implications has no structured pathway. It moves via email or verbal handoff, losing evidence fidelity along the way.

Regulatory blind spots at the intersection

SAR filing decisions require both fraud and AML context. When those contexts live in separate tools, the SAR narrative is always incomplete.

The Verafye Approach

One investigation surface. Both signal sets.

Verafye does not ask fraud teams and AML teams to merge. It connects their data so that any analyst — regardless of which team they sit in — can see the full scope of the risk before closing a case or filing a SAR.

Fraud Prevention

Device, behavioral, and payment fraud signals feed into the same investigation surface as AML data — no separate queue.

AML & Transaction Monitoring

Transaction typologies and entity-level risk scoring surface alongside fraud indicators in every case.

Investigation Intelligence

Alert clustering, case workflows, and evidence packs work across both fraud and AML alert types without separate tooling.

Decision Intelligence

The entity graph resolves relationships across both fraud and AML signal sources — so the connection between a fraud ring and a structuring pattern is visible.

AI Copilot

Case summaries and SAR narratives pull from the full cross-domain evidence pack — not just the fraud or AML slice.

Buyers

Built for the leaders who sit at the intersection

Chief Compliance OfficerHead of FraudBSA OfficerHead of Financial CrimeVP Risk Operations

If your fraud team and AML team are regularly escalating to each other but working in separate systems, a Risk Shadowing Review will show you specifically where those handoffs are creating gaps — using your own transaction and entity data.

Related use cases

Mule Account & Network DetectionTransaction MonitoringInvestigation Workflow Modernization

Get Started

See what your fraud and AML teams are missing

A Risk Shadowing Review runs Verafye's connected detection models on your real data — so you see the gaps, not a demo scenario.

Request a Risk Shadowing Review Explore Platform
Designed for financial institutions and payment platforms
Aligned with evolving regulatory expectations