Detecting fraud patterns across Snowflake and AWS using SageMaker Data Agent

This article demonstrates how Amazon SageMaker Data Agent's new capabilities enable fraud analytics teams to query Snowflake and AWS data sources together, create auto-refreshing materialized views, and generate interactive visualizations without manual ETL or coding.

• SageMaker Data Agent now generates Snowflake-dialect SQL and discovers table schemas automatically, eliminating manual CSV exports and cross-platform data stitching.
• Materialized view management allows analysts to pre-compute expensive aggregations with scheduled refreshes, turning full-table scans into sub-second queries.
• Interactive Vega-Lite charting replaces static matplotlib code, enabling analysts to explore patterns with hover tooltips, zoom, and filtering directly in notebooks.
• Walkthrough demonstrates a fraud investigation: querying Snowflake transactions, landing data into S3 Tables, joining with risk profiles, and visualizing temporal patterns revealing synthetic identity fraud rings.
• Materialized views enable proactive monitoring; the example caught new fraudulent accounts within hours instead of days.

These integrated capabilities reduce fraud investigation time-to-insight by eliminating tool switching and boilerplate code, enabling daily pattern reviews instead of weekly cycles.