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.
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GraphDatabase# #
FinancialCrime#