Employee fraud in financial institutions is a considerable monetary and reputational risk. Studies state that this type of fraud is typically detected by a tip, in the worst case from affected customers, which is fatal in terms of reputation. Consequently, there is a high motivation to improve analytic detection. We analyze the problem of client advisor fraud in a major financial institution and find that it differs substantially from other types of fraud. However, internal fraud at the employee level receives little attention in research. In this thesis, we provide an overview of fraud detection research with the focus on implicit assumptions and applicability. We propose a decision framework to find adequate fraud detection approaches for real world problems based on a number of defined characteristics. By applying the decision framework to the problem setting we met at Alphafin the chosen approach is motivated. The proposed system consists of a detection component and a visualization component. A number of implementations for the detection component with a focus on tempo-relational pattern matching is discussed. The visualization component, which was converted to productive software at Alphafin in the course of the collaboration, is introduced. On the basis of three case studies we demonstrate the potential of the proposed system and discuss findings and possible extensions for further refinements.