  The application of machine learning to support the processing of large
datasets holds promise in many industries, including financial services.
However, practical issues for the full adoption of machine learning remain with
the focus being on understanding and being able to explain the decisions and
predictions made by complex models. In this paper, we explore explainability
methods in the domain of real-time fraud detection by investigating the
selection of appropriate background datasets and runtime trade-offs on both
supervised and unsupervised models.
