Fraud detection in telephone conversations for financial services using linguistic features
Abstract
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.
Cite
@article{arxiv.1912.04748,
title = {Fraud detection in telephone conversations for financial services using linguistic features},
author = {Nikesh Bajaj and Tracy Goodluck Constance and Marvin Rajwadi and Julie Wall and Mansour Moniri and Cornelius Glackin and Nigel Cannings and Chris Woodruff and James Laird},
journal= {arXiv preprint arXiv:1912.04748},
year = {2020}
}
Comments
Published - 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), AI for Social Good Workshop, Vancouver, Canada