The key feature of FinTech software systems is the ability to accurately assess risk in real time, making decisions on high-volume streams of information that are associated with very low latency and are robust to concept drift, and able to be updated without disrupting services. This paper addresses the problem of adaptive risk scoring using a reinforcement learning approach by modeling the risk evaluation problem as a continuous-action Markov Decision Process and continuously optimizing the policy via streaming transactional, behavioral events and outcome driven reward feedback. In addition to the learning algorithm, we also view ARL-CPO as a deployable software architecture that separates... More >