In this blog, we will showcase how to convert several such rule-based detection use cases to machine learning use cases on the Databricks platform, unifying the key players in fraud detection: domain experts, data scientists and data engineers.

We will learn:

• How to create a machine learning fraud detection data pipeline and visualize the data in real time, leveraging a framework for building modular features from large data sets.
• How to detect fraud using decision trees and Apache Spark™ MLlib.
• How to use MLflow to iterate and refine the model to improve its accuracy.