Data Engineering for Fraud Analysis

5 Billion to 1

Data Engineering for Fraud Analysis

In this case study, the SME Team addresses a bank's fraud analysis need by utilizing Azure Data Factory and Snowflake to engineer a solution that improved their time to insight for potential fraud. 

The bank processes about 60 million transactions a day and they need to continuously integrate the new data with historical data, consisting of about 5 billion rows of transactions every 90 days. In order to better predict credit/debit card fraud, analysis must be done on this massive amount of data.

During the on-demand webinar, you will see:

  • The challenges XYZ Bank faced with their data ecosystem.

  • How SME used Azure Data Factory and Snowflake to build a robust data engineering solution.

  • The real-world business impact, including cost reduction and improved fraud detection capabilities.

  • An end-to-end demo of "5 Billion to 1: Data Engineering for Fraud Analysis."