Detect Insurance Fraud with Neo4j Graph Analytics for Snowflake & FastRP Embeddings

Uncover additional fraudulent claims in your insurance data using Neo4j’s Graph Analytics Engine right inside Snowflake. You’ll learn how graph-based techniques can expose complex relationships that traditional methods miss—and how to run everything serverlessly at up to 2× the speed of other open-source solutions.
In this video, you’ll see how to:
Architect a Snowflake + Neo4j Graph Analytics pipeline (raw tables → graph projection → async graph algorithms → results tables)

Run the FastRP (Fast Random Projection) algorithm to turn high-dimensional claim graphs into compact embeddings

Execute K-Nearest Neighbors on those embeddings to flag suspiciously similar claim nodes

Visualize fraud networks in Snowflake and build dashboards, alerts, or further analysis on your results

#GraphAnalytics #Neo4j #PredictiveMaintenance #Snowflake #MachineLearning #GDS #FactoryOps #DataScience #GraphAlgorithms

Explore Neo4j Graph Analytics for Snowflake → https://neo4j.com/snowflake
Learn more about Neo4j AuraDB → https://neo4j.com/cloud/aura
Access the code and dataset on GitHub: → https://github.com/neo4j-product-examples/snowflake-graph-analytics/tree/main/insurance-fraud