Fraud is as old as humankind and appears in many types and forms. Popular examples are credit card fraud, tax evasion, identity theft, insurance fraud, counterfeit, click fraud, antimoney laundering, and payment transaction fraud. In earlier research we defined fraud as an uncommon, wellconsidered, imperceptibly concealed, timeevolving, and carefully organized crime. Nowadays, fraud is typically tackled using stateoftheart analytical techniques with many accompanying challenges. It is the purpose of this presentation to highlight various challenges that we believe prioritize high on the agenda of contemporary fraud analytics models. We do this by reviewing fraud analytics from a data, model, performance, and deployment perspective. Throughout the talk, I will extensively refer to my more than two decades of research and consulting experience on the topic and use various case studies and examples for clarification.