Fraud detection in telephone conversations for financial services using linguistic features

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.

Speaker bio: Nikesh Bajaj is a Postdoctoral Research Fellow at the University of East London, working on the Innovate UK funded project - Automation and Transparency across Financial and Legal Services, in collaboration with Intelligent Voice Ltd. and Strenuus Ltd. The project includes working with machine learning researchers, data scientists, linguistics experts and expert interrogators to model human behaviour for deception detection. He completed his PhD from Queen Mary University of London in a joint program with University of Genova. His PhD work is focused on predictive analysis of auditory attention using physiological signals (e.g. EEG, PPG, GSR). In addition to research, Nikesh has 5+ years of teaching experience. His research interests focus on signal processing, machine learning, deep learning, and optimization.