In such ecosystems, fraud is often network-driven rather than isolated. Straive applied graph-based analytics to map relationships across entities—customers, devices, merchants, and transactions—enabling the identification of coordinated fraud patterns.
The solution combined:
- Relationship-based analysis using graph models.
- Real-time detection embedded within transaction workflows.
- Anomaly detection across interconnected behavioral signals.
- Continuous monitoring across the payments lifecycle.
This approach allowed for faster and more accurate identification of fraud, improving decision-making without disrupting transaction flows.
The outcomes included:
- Up to 27% reduction in fraud losses.
- Multimillion-dollar cost savings.
- Improved visibility into complex fraud networks.
This case reflects how financial systems in modern commerce environments are evolving from rule-based processing to intelligence-driven, adaptive systems.