Within fintech catching fraudsters is one of the primary opportunities for us to use streaming applications to apply ML models in real-time. This talk will be a review of our journey to bring fraud decisioning to our tellers at Capital One using Kafka, Flink and AWS Lambda. We will share our learnings and experiences to common problems such as custom windowing, breaking down a monolith app to small queryable state apps, feature engineering with Jython, dealing with back pressure from combining two disparate streams, model/feature validation in a regulatory environment, and running Flink jobs on Kubernetes.