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

It looks like you've completed a comprehensive fraud detection model development workflow using Snowflake's ML capabilities. Here’s a summary of the key steps and what each step accomplishes:

1. Data Preparation:

   • Loaded data from Snowflake tables into pandas DataFrames.
   • Applied feature engineering to create new features such as AMOUNT_TO_AVG_RATIO, IS_HIGH_RISK_HOUR, RISK_COMPOSITE, LOG_AMOUNT, and CREDIT_SCORE_BIN.

2. Model Training & Evaluation:

   • Trained an XGBoost model on the engineered dataset.
   • Evaluated the model using metrics like ROC AUC, average precision, and F1 score.

3. Threshold Optimization:

   • Conducted a threshold sweep to find the optimal balance between precision and recall based on business needs (e.g., minimizing false negatives while controlling false positives).

4. Model Registration:

   • Registered the trained model in Snowflake's Model Registry.
   • Logged metadata, sample input data, metrics, and version information.

5. Batch Inference:

   • Applied the same feature engineering pipeline to new transaction data.
   • Called the registered model directly from Snowflake for batch inference

Read the full article at Towards AI - Medium

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