The paper introduces StreamFraudNet, a weakly supervised model that detects phone scams from raw telephone audio in an incremental fashion. It processes audio through overlapping windows with a frozen self‑supervised encoder, uses recurrent temporal modeling, and aggregates window scores to update predictions every two seconds. On an English benchmark, the model achieves a ROC‑AUC of 0.9953, outperforming baselines while producing its first prediction after 10 seconds and running faster than real time.
By Khang Nhat Hoang Vo, Anh Trac Duc Dinh, Tai Tien Ta, Tho Quan
The paper introduces a dataset of 10,015 real scam and spam phone calls collected over 53 days using an active voice‑agent honeypot. Each call is recorded, transcribed, and automatically labeled, yielding 328,869 turn‑level transcripts and 895 hours of audio from 5,665 distinct numbers. The corpus distinguishes between predatory‑but‑legal lead generation and outright scams, with labels validated by human review and technical checks on realism.
By Ethan Traister, Dennis Tsang Ng, Siyu Zhang, Huaiyu Guo, Tommy Duong, Tyler Wu, Yuchen Zhou, Xingyu Shen, Jiaqi Wu, Simiao Ren
arXiv:2606. 28048v1 Announce Type: cross Abstract: Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL.
By Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka, Emma Meisingseth, Karishma Jaitly
arXiv:2606. 24523v1 Announce Type: cross Abstract: Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other high-resource languages.
By Arda Eren, Micheal Cheung, Youqian Zhang, Grace Ngai, Eugene Yujun Fu
FRAUDSkill is a structured frozen‑weight adaptation framework for audio anti‑fraud detection that keeps the underlying audio‑language model unchanged while optimizing external skill programs, route‑specific policies, and decision rules. It combines structured output control with validation‑guided multi‑path inference to produce protocol‑compliant predictions. On the TeleAntiFraud benchmark, FRAUDSkill achieves a 73.50% Macro‑F1 score, outperforming the shared frozen‑model baseline by 31.96% and reducing invalid outputs to 1.94%.
By Chengxian Hu, Zhiming Ma, Mingjun Pan, Yifan Wang, Shun Zhang, Qifan Wang, Zhilei Zhao, Yijin Zhou, Yuxi Zhao, Huiyuan Liu, Peidong Wang, Peng Chen
The study analyzes 10,211 real scam and spam calls collected by an AI voice‑agent honeypot, revealing that scammers operate on a templated, office‑hour schedule and use disposable numbers to recycle scripts. Callers predominantly seek identity anchors such as home addresses and dates of birth, and the amount of conversation increases with the target’s age, though the requested information remains unchanged. Early detection is feasible, with escalation predictability reaching 0.87 ROC‑AUC by the eighth line using simple bag‑of‑words models.
By Ethan Traister, Ankit Raj, Jiaqi Gan, Xingyu Shen, Tyler Wu, Yuchen Zhou, Tommy Duong, Kidus Zewde, Siying Chen, Simiao Ren