Sponsored by: Neo4j | Financial Crime Hides Between the Rows

Fraud rings do not announce themselves in a single row. They hide two or three hops away from the obvious suspects, woven into transactions that look ordinary until you trace the full network. The analyst tools stay the same, what changes is what the data reveals. Your Databricks Lakehouse already holds the data. What it lacks is a way to traverse it as a connected graph. We walk through a live fraud investigation and show how Neo4j turns days of manual analysis into a query Databricks Genie answers in seconds. The hidden patterns do not stay hidden once they become columns. Neo4j scores every account; those scores land in your Lakehouse as ordinary dimensions. Databricks Genie needs no changes; it queries graph scores the same way it queries region or balance. Before enrichment: a flat list. After: accounts at the center of distinct fraud communities. Attendees leave with the notebook and graph data model on GitHub to replicate this on their Delta Lake data.