Case Study: Gilead Sciences strengthens pharmaceutical fraud detection with Neo4j Graph Analytics

Gilead Sciences combats $431 billion pharmaceutical fraud threat with Neo4j

Gilead Sciences faced the challenge of combating pharmaceutical fraud, product diversion, and program misuse, a $431 billion global threat. Their existing methods, which relied on analyzing isolated datasets, were difficult to scale and obscured the complex relationship patterns critical to investigations. To address this, Gilead implemented a graph analytics and AI-driven intelligence platform using the vendor Neo4j and its product AuraDB.

The Neo4j solution enabled Gilead to unify previously disconnected data sources, modeling entities and their relationships directly. This allowed investigators to visually explore complex networks, significantly improving detection rates and reducing false positives compared to relational databases. While specific metrics are not publicly shared, the platform provides greater clarity for building defensible cases, ultimately enhancing patient safety and program integrity.

Gilead Sciences