A challenge with fraud-detection problems in many cases is the lack of any meaningful collection of labelled data for supervised-learning approaches to work. Two things practitioners do to tackle the problem are statistical profiling, usually via domain-specific business rules, and statistical outlier detection, sometimes augmented with non-trivial models of what constitute “normal” behaviour. There is … More A Framework to Detect Waste and Fraud in Health Insurance