  Detecting the elements of deception in a conversation is one of the most
challenging problems for the AI community. It becomes even more difficult to
design a transparent system, which is fully explainable and satisfies the need
for financial and legal services to be deployed. This paper presents an
approach for fraud detection in transcribed telephone conversations using
linguistic features. 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. We demonstrate the results on real-world
financial services data using simple, robust and explainable classifiers such
as Naive Bayes, Decision Tree, Nearest Neighbours, and Support Vector Machines.
