Although several approaches for service identification have been defined in research and practice, none of them considers the potential of fully automating the associated phases. As a result, users have to invest a substantial amount of manual work. In this paper, we address the problem of manual work in the context of service identification and present an approach for automatically deriving service candidates from business process models. Our approach combines different analysis techniques in a novel way in order to derive ranked lists of service candidates. The approach is meant to be a useful aid for enabling business and IT managers to quickly spot reuse potential in their company. We demonstrate the usefulness of our approach by reporting on the results from an evaluation with a process model collection from industry.