Fragmented relationships
Relevant entities, accounts and counterparties can be difficult to examine as one connected picture.
AI-Native Financial Crime Intelligence
Advancing Financial Crime Intelligence Through AI.
Fraudera.AI develops proprietary AI and machine learning technology to help financial institutions understand complex financial relationships, interpret risk signals, and investigate financial crime.
Our first product, Fraudera.AI Copilot, brings this intelligence to Anti-Money Laundering investigations.
Built for human-led investigation. Findings provide context for review; they do not determine guilt or criminality.
Who we help
Investigation intelligence for financial crime teams.
Designed for AML, Compliance, Financial Crime and Risk teams across banks, fintechs, payment service providers and other financial institutions.
Banks
Fintechs
Payment service providers
Financial institutions
The investigation challenge
Risk signals are only useful when teams can understand the context.
Disconnected context
Evidence and investigative context may be spread across systems and hand-offs.
Signals that need interpretation
Surfaced risk signals still require clear reasoning and analyst judgement.
Coordinated casework
Assignments, case status and team capacity need consistent visibility.
Investigation workflow
Move from a finding to a structured, human-led review.
1Finding
2Relationship Context
3Explainable Risk Review
4Case Workflow
Illustrative concept — not a product screenshot
Network intelligence
Examine the relationships around a finding.
Relationship context helps analysts explore how entities and counterparties connect, then decide what deserves further review. A connection is context—not proof of suspicious or criminal activity.
- Explore connected entities
- Review counterparty context
- Keep analyst judgement central
Fraudera.AI Copilot
Capabilities for institution-scoped investigation work.
Fraudera.AI Copilot brings AML findings, relationship intelligence, case workflows, and management oversight into a connected investigation experience.
Institution-scoped AML findings
Bring findings into a controlled review process within an institution’s investigation boundary.
Network intelligence
Explore relevant relationships between entities, accounts and counterparties.
Risk Control Tower
Give authorised managers a consolidated view of investigation activity and operational context.
Institution case management
Organise findings and investigation work within the institution’s case workflow.
Analyst assignment and reassignment
Route investigation work and update ownership as team needs change.
Team workload
Review assigned work to support informed team coordination.
Management oversight
Give Risk Managers and Supervisors a clearer view of investigation work.
Case visibility, analyst assignments and team workload can support informed coordination while investigators retain responsibility for their reviews.
Case visibilityReview status
AssignmentsTeam ownership
WorkloadCapacity context
The Intelligence Behind Fraudera.AI
Advancing Financial Crime Intelligence Through Proprietary AI.
At Fraudera.AI, artificial intelligence is not simply an integration. Our team develops, trains, and evaluates proprietary machine learning models, combining graph-based intelligence, temporal analysis, and ongoing research to advance financial crime investigation technology.
Pursuing frontier AI research and state-of-the-art machine learning for financial crime intelligence.
Proprietary
AI/MLGraph intelligenceTemporal analysisExplainability
AI/MLGraph intelligenceTemporal analysisExplainability
Proprietary AI/ML Models
Internally developed, trained, evaluated, and iteratively improved models focused on financial crime intelligence.
Graph & Temporal Intelligence
Relationship-based and temporal approaches for understanding financial activity and risk patterns.
Research-Driven Engineering
Experimentation, evaluation, explainability, and domain-specific model development led by our technical team.
Fraudera.AI
AI-native intelligence for financial crime.
Fraudera.AI develops proprietary artificial intelligence and machine learning technology to help financial institutions understand complex financial relationships, interpret risk signals, and support human-led investigations. Our first product, Fraudera.AI Copilot, focuses on Anti-Money Laundering.
Deployment & security assessment
Architecture starts with the institution’s requirements.
We work with financial institutions to assess deployment architecture, data residency, access controls, and integration requirements. Hosting arrangements are evaluated against each institution's security and operational needs.
Discuss your requirementsResearch & insights
Thinking behind the work.
Financial Intelligence
How Relationship Intelligence Transforms Financial Crime Investigations
1 min readProduct walkthrough FAQ
What to expect from an initial conversation.
What will be demonstrated?
A guided discussion of Fraudera.AI Copilot’s current AML investigation workflow, including conceptual examples of findings, relationship context and case coordination.
Who should attend?
AML, Compliance, Financial Crime and Risk stakeholders, together with relevant technology or security colleagues.
Is sensitive customer data required for an initial discussion?
No. An initial discussion can focus on workflows and requirements without customer or transaction data.
Can deployment and security requirements be discussed?
Yes. We can discuss your requirements and assessment process without implying a deployment model is already approved or universally available.
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