From Unstructured Rules to Actionable Graph Previsant's Approach to Improper Payment Detection with

Detecting improper payments at scale starts with a deceptively hard problem: extracting adjudication and payment rules from thousands of complex, inconsistent documents where those rules live as unstructured text. In this Community Call, Satya Sachdeva and Doug Ramsey, co-founders and CTO, Chief Product Officer, Head of Data Science and CEO respectively of Previsant will demonstrate how their payment integrity platform uses Memgraph to solve this problem. The session will cover how rules are extracted from source documents and modelled as entities and relationships in the graph, how complex rule dependencies are surfaced across a corpus of thousands of documents, and how a natural language interface lets analysts query the resulting rule graph without writing Cypher. The walkthrough will include rule document ingestion (batch and real-time), schema optimisation for rule relationships, and entity-relationship management from source documents — all running on Memgraph in production. Why attend: If your team is working on payment integrity, claims adjudication, or fraud, waste, and abuse prevention, this session shows a production approach to a problem that most organisations are still solving manually or with brittle rule engines. Previsant brings deep domain expertise in healthcare insurance and payment integrity — they have built and deployed this platform against real-world rule corpora, not synthetic demos. Memgraph provides the in-memory graph engine underneath, handling the real-time ingestion, entity-relationship modelling, and multi-hop traversal that make it possible to surface rule conflicts and dependencies across thousands of documents at query speed. Together, they demonstrate what happens when domain knowledge and graph infrastructure are properly combined — and why vector search or document retrieval alone cannot solve this class of problem. About Memgraph: Memgraph is a high-performance, in-memory graph database that powers real-time AI context. It serves as the graph engine for GraphRAG pipelines, AI memory systems, and agentic workflows - delivering sub-millisecond multi-hop traversals with full provenance for any system that needs structured, connected context alongside semantic search. The same architecture that makes Memgraph the context layer for AI also drives real-time graph analytics across fraud detection, network analysis, infrastructure monitoring, and other operational use cases where speed and connectivity matter. Website: https://www.memgraph.com Twitter: https://www.twitter.com/memgraphdb LinkedIn: https://www.linkedin.com/company/memgraph Facebook: https://www.facebook.com/memgraph