In collaboration with linguistics and expert interrogators, we present an approach for fraud detection in transcribed telephone conversations. The proposed approach exploits the syntactic and semantic information of the transcription to extract both the linguistic markers and the sentiment of the customer's response. The results of the proposed approach are demonstrated with real-world financial services data using efficient, robust and explainable classifiers such as Naive Bayes, Decision Tree, Nearest Neighbours, and Support Vector Machines.