LLM-based turn-by-turn analysis of phone conversations can flag scams in real time with high recall, and an optional 'UNCERTAIN' label trades recall for precision.
Distinguishing Scams and Fraud with Ensemble Learning
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abstract
Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LLM performance on user scam queries, but currently the corpus does not distinguish between scam and non-scam fraud. We developed an LLM ensemble approach to distinguishing scam and fraud CFPB complaints and describe initial findings regarding the strengths and weaknesses of LLMs in the scam defense context.
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"It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams
LLM-based turn-by-turn analysis of phone conversations can flag scams in real time with high recall, and an optional 'UNCERTAIN' label trades recall for precision.