Building a Production-Grade Fraud Detection Pipeline Inside Snowflake — End to End

How Snowflake ML, XGBoost, the Model Registry, and ML Observability let you go from raw transactions to a monitored fraud scorer without ever leaving your data platform Fraud doesn’t wait. A transaction hits in milliseconds, and your window to flag it before the funds move is even shorter. Yet most fraud-detection stacks still look like an archaeology dig - raw data in a warehouse, feature engineering on someone’s laptop, a pickled model emailed to a DevOps team, and a monitoring dashboard bolted on as an afterthought.