Fraud detection in telephone conversations for financial services using linguistic features
It addresses fraud detection for financial and legal services, focusing on explainability, but is incremental as it applies existing methods to a specific domain.
The paper tackled fraud detection in transcribed telephone conversations for financial services by using linguistic features from syntax and semantics, achieving results with simple, explainable classifiers like Naive Bayes and SVM on real-world data.
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.