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Fraud detection in telephone conversations for financial services using linguistic features
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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.
Forward citations
Cited by 2 Pith papers
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Advanced Real-Time Fraud Detection Using RAG-Based LLMs
A RAG-based LLM pipeline classifies phone calls as fraudulent by comparing transcripts against company policy documents, with a reported 97.98% accuracy on synthetic calls.
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Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment
A hybrid chat-fraud pipeline with classifier, drift detector, and LLM judge is described, but only the classifier stage is measured and the dataset citation is incorrect.
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