Hyper graph is a data structure that captures many-to-many relations. It comes up in various contexts, one of those being the task of detecting fraudulent users of an on-line system given known associations between the users and types of activities they take part in. In this work we explore three approaches for applying general-purpose machine learning methods to such data. We evaluate the proposed approaches on a real-life dataset of customers and achieve promising results.