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
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
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