In transaction authentication and authorization, multiple decisions must be made, which are currently often supported by separate models arranged in a pipeline. A more integrated approach can lead to better-informed decisions, but requires complex optimisation in a high-dimensional space. In this project, we use reinforcement learning to address this optimisation problem, focusing on challenges such as high-dimensional action spaces, offline learning and evaluation, and balancing multiple objectives.
