Unmasking fraud networks: How graph-grounded Copilots stop P2P fraud in Microsoft Fabric

Microsoft Fabric and Neo4j Graph Intelligence are presented as a way for financial institutions to improve detection of organized peer-to-peer payment fraud by converting siloed transactional and customer data in OneLake Delta tables into connected property graphs. Conventional SQL-based AI agents can struggle with multi-hop relationships and generate false positives by treating common connections, such as public Wi-Fi addresses, the same as rare, high-confidence links like shared debit cards, phone numbers, or repeated payment paths. Neo4j’s graph algorithms, including Weakly Connected Components and PageRank, can filter high-traffic hubs, score meaningful relationships, identify likely fraud-ring clusters, and return concise evidence-based results. The integrated Fabric workflow allows teams to run graph analysis in notebooks, write risk scores and community identifiers back to Delta tables, and provide Fabric Data Agents with relationship-aware context while retaining enterprise controls such as single sign-on, Purview governance, and tenant isolation.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.