Anonymous US Insurance Company

This multinational P&C insurer relied on rules-based engines and machine learning models that, in isolation, could not efficiently traverse the highly interconnected relationships inherent in insurance data — individuals, vehicles, policies, and claims linked across organizational silos. Predefined rules required constant manual updates as fraud tactics evolved, making the system perpetually reactive. The disconnect between data silos prevented analysts from seeing the full network of relationships that sophisticated fraud rings exploit. The consequence was a high false-positive rate: legitimate claims were flagged unnecessarily, overburdening investigation teams, delaying claim processing, and eroding customer satisfaction — all while genuinely fraudulent claims slipped through.

Rather than replacing their existing fraud detection stack, the company layered Memgraph — an in-memory graph database — on top of their rules engine and predictive ML models as a targeted enhancement. Memgraph modeled the full entity graph connecting claimants, policies, vehicles, addresses, and claims, enabling deep path analysis, community detection, and centrality algorithms to surface hidden fraud networks that neither rules nor ML alone could identify. The in-memory architecture provided the real-time processing speed required to analyze new claims as they arrived. Memgraph Lab gave fraud analysts a unified, real-time visualization of interconnected data across previously siloed systems — for the first time presenting the data in a form analysts could immediately act on.

The implementation delivered a 135% increase in fraud detection efficiency across claim types, driven by graph analytics uncovering patterns invisible to the prior system. Key outcomes include:

• 7-figure prevented losses: The Head of Advanced Analytics stated that had Memgraph been deployed earlier, the company would have saved more than seven figures in missed fraud on historical datasets alone.
• Substantial false-positive reduction: Layering graph analytics onto existing ML models cut unnecessary claim investigations, improving both investigator throughput and claimant experience.
• Real-time responsiveness: The ability to visualize and query the entity graph as new claims arrived allowed the fraud team to respond to emerging patterns rather than discovering them retrospectively.

• Graph databases are uniquely suited to P&C fraud detection because insurance data is inherently relational — fraud rings exploit connections between entities that flat tables and rule sets cannot efficiently expose.
• Deploying graph analytics as an additive layer over existing rules engines and ML models, rather than a replacement, reduces implementation risk and delivers compounding detection improvements.
• In-memory processing is a prerequisite for real-time fraud detection; disk-based graph solutions introduce latency that allows ongoing exploitation before fraud is identified.
• Unified data visualization across silos can itself be transformative — analysts gaining a clear, connected view of their data often identify fraud patterns that no automated rule would have caught.