We investigate possible improvements in online fraud detection based on information about users and their interactions. We develop, apply, and evaluate our methods in the context of Skype. Specifically, in Skype, we aim to provide tools that identify fraudsters that have eluded the first line of detection systems and have been active for months. Our approach to automation is based on machine learning methods. We rely on a variety of features present in the data, including static user profiles (e.g., age), dynamic product usage (e.g., time series of calls), local social behavior (addition/deletion of friends), and global social features (e.g., PageRank). We introduce new techniques for pre-processing the dynamic (time series) features and fusing them with social features. We provide a thorough analysis of the usefulness of the different categories of features and of the effectiveness of our new techniques.