Paysign, Inc. (PAYS) has developed a purpose-built large language model at the core of its ClaimGuardRx platform, targeting fraud and diversion in patient affordability programmes.
Bryan Dennison, Senior Vice President of Sales and Product at Paysign, has outlined the technical foundation that separates ClaimGuardRx from conventional fraud detection tools in the pharmaceutical sector.
Unlike many platforms that repurpose general-purpose artificial intelligence models, ClaimGuardRx is trained specifically on patient affordability claim patterns, giving it a distinct analytical edge.
Dennison explains that this domain-specific training allows the model to identify subtle diversion tactics that a broader, less specialised system would likely miss entirely.
The platform’s detect-and-respond design is central to its value proposition, significantly shortening the window between identifying a threat and taking corrective action against it.
A voice-driven interface allows nontechnical users to pose plain-language questions directly to the system, removing the barrier that complex data environments typically present to non-specialist staff.
In response to those queries, the platform delivers analytical insights, supporting data, and recommended next actions, all within seconds of a question being submitted.
This capability means that manufacturers can keep pace with evolving diversion tactics without requiring dedicated data science teams to interpret results or determine responses on their behalf.
Paysign has also outlined a near-, mid-, and long-term roadmap for expanding ClaimGuardRx’s capabilities, signalling continued investment in the platform’s development well beyond its current feature set.
The roadmap suggests the company intends to deepen the model’s ability to detect emerging fraud patterns as bad actors adapt their methods in response to tightening pharmaceutical programme controls.
ClaimGuardRx represents a broader industry shift toward purpose-built artificial intelligence tools designed to address the specific complexities of pharmaceutical patient support and affordability programmes.
For drug manufacturers operating copay and patient assistance schemes, the stakes of undetected diversion are significant, involving both financial exposure and potential compliance and regulatory consequences.
Paysign’s approach of building from the ground up, rather than adapting an existing model, reflects a growing recognition that general-purpose tools often lack the contextual depth required in highly regulated industries.