Financial crime runs as a network, but the teams fighting it work in silos. A new product aims to close that gap with graphs 🕸️ Introducing Neo4j GraphAware Financial Crime Intelligence 🧩 Overview A financial crime platform that connects detection, investigation, and decisions on one graph foundation built from internal and external data. ⚠️ The problem ・Siloed tools and data show only isolated events ・Weak signals go unnoticed until after the damage is done 🛠️ How it works ・Unify: a persistent, entity-resolved knowledge layer ・Signal: graph detection of shared infrastructure, identity reuse, circular flows, and layering ・Alert: correlated activity with context ・Investigate: trace multiple hops and record evidence and rationale 🔎 Use cases ・Fraud monitoring: reveal shared devices and money mule networks ・AML/KYC: trace beneficial ownership through corporate structures ・Insurance fraud: expose organized networks behind seemingly unrelated claims 🤝 Highlights "Human-led, AI-accelerated": investigators and AI agents reason over the same knowledge layer. It works alongside existing detection engines, so teams can start with one problem. #GraphDatabase# #FinancialCrime#