Graph neural networks are transforming pharmaceutical fraud detection by revealing hidden networks of bad actors that traditional systems cannot identify.
Gilead Sciences Inc. implemented graph neural networks with Neo4j to move beyond isolated transaction analysis to relationship-based detection, enabling the company to surface fraud schemes at scale across its global business — a capability that was previously limited by manual analyst work and data fragmentation.
What happened
Graph neural networks are being deployed to detect pharmaceutical counterfeiting and fraud by revealing hidden relationships between bad actors — work that Thomas Luu, director of global product security at Gilead Sciences Inc., describes as exposing networks that traditional systems miss. Gilead's team now runs a three-layer detection model combining rules-based logic, traditional machine learning, and graph neural networks to surface relationship-based schemes.
Why it matters
Before adopting graph technology, Gilead's investigators relied on manual comparison across fragmented data sets, a process limited by human capacity and individual analyst expertise. Graph neural networks automatically cluster main fraud actors with lower-volume auxiliary players whose signals would otherwise stay hidden in aggregate data, making fraud detection faster and scalable across Gilead's global commercial business.
What to watch
The graph-based approach produces visually intuitive relationship maps that make findings easier to explain to non-technical investigators, addressing a longstanding challenge in fraud investigation where sophisticated fraudsters traditionally hide their activity in aggregate patterns and among peers.
Graph neural networks are reshaping fraud detection in pharmaceuticals by moving beyond transaction-level analysis to reveal entire hidden networks of bad actors. At Gilead Sciences Inc., director of global product security Thomas Luu oversees investigations into counterfeit drugs and fraud across the company's global commercial business. Historically, his team's approach relied on manual comparison across fragmented data sets — a labor-intensive process constrained by individual analyst expertise and unable to scale with increasingly sophisticated fraud schemes.
Gilead's current system deploys a three-layer detection model. The first layer applies rules-based logic to identify known fraud patterns; the second uses traditional machine learning to detect statistical anomalies; the third introduces graph neural networks powered by Neo4j to uncover relationship-based schemes that the first two layers cannot surface. As Luu explained, "Fraud doesn't happen with just one transaction. It happens across a lot of entities, and a lot of these entities are hidden." The breakthrough came after the team converted relational data into graph form, a prerequisite for enabling graph neural networks to operate at scale.
The operational payoff extends beyond detection speed. Clustering algorithms built into the graph automatically group a main fraud actor with lower-volume auxiliary players whose signals would otherwise remain buried in aggregate data — precisely where sophisticated fraudsters conceal their activity. Luu observed that "the best fraudsters are the ones that's hiding in the averages, and they're hiding amongst peers and they're concealing their activity." The visual structure of relationship graphs also simplifies communication: findings presented as node-and-edge relationships are more intuitive for investigators without technical backgrounds than traditional statistical output, reducing friction in the handoff from data analysis to investigation and enforcement.
Graph neural networks represent a structural shift in how enterprises detect fraud — not simply by processing data faster, but by organizing data in a way that exposes relationships humans and traditional machine learning systems cannot easily see. In pharmaceutical anti-counterfeiting work, this distinction is operationally critical: fraudsters traditionally succeed by distributing their activity across multiple entities and hiding in aggregate patterns, making them difficult to identify through rule-based logic or statistical anomaly detection alone. Gilead's three-layer approach reflects an emerging best practice: combining rules, traditional machine learning, and graph methods creates redundancy that prevents any single technique from becoming the sole basis for investigation.
The automation enabled by graph data structure — particularly the automatic clustering of main actors with auxiliary players — directly addresses a scaling problem that manual analyst review cannot solve. As Luu notes, the shift unlocks the ability to analyze data "to a level and to a scale that was not possible before because of human limitations." The visual intuitiveness of relationship graphs also reduces friction in the investigation handoff, where technical analysts must communicate findings to non-technical investigators, compliance teams, and legal counsel.
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