Fraud networks often go undetected because traditional systems assess transactions individually, losing the connections between accounts, devices, merchants and synthetic identities. This is particularly problematic with mule networks, leading to missed fraud, slow investigations and fragmented compliance reporting as regulatory expectations continue to rise.
Graph technology connects the entire fraud lifecycle in one shared data model, preserving relationships across detection, investigation and reporting while feeding confirmed fraud outcomes back into detection. In production at large banks, this approach has delivered +200% improvement in fraud detection at 10 ms query latency.
This webinar shows how graph technology connects the full fraud lifecycle in one shared data model:
- Real-time detection in milliseconds
- Investigator-led case review and network analysis with Hume (Financial Crime Intelligence)
- Explainable evidence for compliance and SAR/STR workflows
- Continuous model improvement from confirmed fraud outcome
Proof at scale:
- +200% fraud detection improvement
- 10ms production query latency
- 250M nodes in production
- 2.2B relationships managed
Join us for a practical walkthrough of the complete fraud lifecycle, from transaction scoring to investigator workflows and continuous model improvement.
Who should attend
- Industries: Financial services
- Hierarchy level: Senior decision makers: C-level (CEO, COO, CIO, CTO), VP and Director, plus managers of data science teams
- Job function: IT leadership (CIO, CTO, VP Engineering, Director of IT), business leadership (CEO, COO, VP, Director) and managers of data science teams
Learning Points
- Why connected data improves fraud detection accuracy
- How graph enrichment fits inside the 200ms transaction window
- How graph-enriched alerts differ from traditional alerts
- How investigators use Hume to uncover mule networks and synthetic identities faster
- Why explainability matters for model governance (DORA, GDPR and the US SR 11-7 as a reference point)
- How feedback loops improve future detection automatically
- Production benchmarks from large-scale banking deployments
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