Fraud Detection Data Pipeline & Quality Platform

Context

A financial services organization relied on machine learning models to detect fraud in high-volume transaction streams. The models were only as reliable as the data feeding them.

The problem

Late, missing, or corrupted data caused model instability, false positives, and blind spots. There was no automated guardrail to stop bad data from reaching production.

What I built

I built a data quality and monitoring layer around the fraud pipelines to validate data before it reached the models and to alert teams when something went wrong.

How it worked

• HiveQL and Python pipelines generated model-ready datasets
• Automated checks validated row counts, nulls, schema, and drift
• Failures triggered alerts and blocked bad data
• Dashboards provided visibility to fraud and engineering teams

Impact

• Prevented bad data from degrading ML performance
• Reduced production incidents related to data issues
• Improved trust in fraud detection outputs