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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
TeleAntiFraud 2.0 is a monthly‑frozen, audio‑based benchmark for telecom fraud detection that incorporates newly observed scam patterns while preserving earlier test sets. It uses a Mixed‑Tree Anti‑Fraud Generation Pipeline to create profile‑grounded scenarios, expands them into mixed‑tree dialogues, and renders validated speech for 900 Chinese calls (600 fraud, 300 near‑domain non‑fraud) each month. Experiments show that classifiers perform well against unrelated negatives but drop significantly against near‑domain negatives, highlighting the need for near‑domain construction and collapse‑aware reporting in realistic evaluation settings.
arXiv:2606. 28002v1 Announce Type: cross Abstract: Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholders.
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.
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.
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.
The paper "Beyond EER: Multi-Dimensional Evaluation of Information Leakage in Speaker De-Identification" argues that evaluating speaker de-identification systems solely by Equal Error Rate (EER) is insufficient. It proposes a holistic framework using five complementary metrics—EER, soft biometric leakage score, cumulative match characteristic re-identification analysis, canonical correlation analysis with Procrustes embedding alignment, and intelligibility via word error rate and semantic similarity—to capture independent dimensions of information leakage. Experiments on five IARPA ARTS SDID systems show that these metrics reveal leakage that a single metric would miss.