Fraud is a critical issue in financial services, at an estimated $15-25 Billion size for the industry in 2017. Fraud detection and management has typically been performed using a combination of business rules and traditional machine learning. However, such solutions invariably generate a large number of costly false positives. We will present a solution architecture that combines automated machine learning, deep feature synthesis and streaming analytics to power a scalable, deployable deep fraud detection system.
