Finding Circular Payments with Neo4j Virtual Graph

See how to enable graph-powered insights on your existing cloud data warehouse, without having to move your data. Find circular payments on transactional Snowflake data and combine results with customer data in AuraDB, all in one federated query.

INFO: https://neo4j.com/blog/graph-database/introducing-neo4j-virtual-graph-graph-reasoning-on-the-data-you-already-have/

Key highlights include:

* **Zero Data Movement**: Access insights on your data warehouse without disrupting your existing infrastructure.
* **GenAI Integration**: Rapidly generate robust graph models from your existing data tables with a single click.
* **Cycle Detection**: Use simple Cypher queries translated automatically into SQL to find closed-loop transactions.
* **Federated Queries**: Seamlessly merge results from Snowflake and AuraDB to associate transaction IDs with specific customer profiles and high-risk alerts.

00:10 Introduction of the virtual graph feature for bringing graph-powered insights to existing data warehouses.

01:01 Demonstration of using generative AI to create a graph model with one click.

01:34 Running a simple query against a virtual graph to find transaction paths between senders and beneficiaries.

01:59 Identification of a circular payment involving three accounts moving approximately $7,300 in a closed loop.

02:12 Use of a federated query to combine virtual graph results with customer master data to identify the individuals involved in the transactions