Anatomy of a Scam Call: What 10k real scam calls reveal about phone scammers

A study of 10,211 real scam and spam calls collected over 54 days by an AI voice-agent honeypot found that scammers spend about 15% more conversational turns per decade of a target's apparent age (rate ratio 1.15, 95% CI 1.08-1.23; p=0.005) but do not change what they ask for, with 26.3% of calls reaching a request for sensitive information. The researchers, who posted the paper on arXiv on Aug 25, 2026, also found that scam operations keep office hours (6.6x more calls per weekday than weekend day) and that escalation is predictable from opening lines alone at 0.72 ROC-AUC from the first line and 0.87 by the eighth.

Computer Science Cryptography and Security Submitted on 25 Aug 2026 Title:Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate View PDF /pdf/2608.24127 HTML experimental https://arxiv.org/html/2608.24127v1 Abstract:Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls -- 913 hours of audio and 330,956 transcribed turns from 5,780 distinct numbers -- collected over 54 days by an AI voice-agent honeypot that answered callers and kept them talking, and introduced in a companion data descriptor. We separate outright scams, which solicit sensitive information, from the larger stream of predatory but legal lead generation "spam" that feeds them. Scam operations keep office hours 6.6x more calls per weekday than weekend day ; thousands of disposable numbers run a small catalog of recycled scripts thirty opening clusters, half the traffic in the top five ; and callers solicit identity anchors -- a home address and a date of birth -- far more often than payment credentials, pressing through persistence and manufactured authority rather than overt threats. Our central experiment asks: does it matter who picks up? Every seeded lead carried one of ten fictitious identities drawn uniformly at random, so the identity a fraud operation reaches is fixed before the caller exists. Across 1,823 randomized calls, scammers spent about 15% more conversational turns per decade of the target's apparent age rate ratio 1.15, 95% CI 1.08-1.23; randomization p = 0.005 -- yet what they asked for did not change 26.3% of calls reached a request for sensitive information; odds ratio 0.99 per decade, 95% CI 0.90-1.08 . A second experiment casts early detection as a benchmark: from a scammer's opening lines alone, on a caller-disjoint split, escalation is predictable at 0.72 ROC-AUC from the first line and 0.87 by the eighth, and a plain bag-of-words classifier matches a fine-tuned on-device language model. Telephone fraud emerges as a templated industry that varies how hard it works a target, but not what it wants. Current browse context: cs.CR References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .