ICFD-31k: A Large-Scale Dataset and Benchmark for Real-Time Conversational Fraud Detection

摘要

The proliferation of sophisticated telephone scams poses a significant societal and economic threat, impacting diverse linguistic contexts in a country like India. Furthermore, the lack of large-scale, publicly available datasets remains a critical barrier impacting research on robust, real-time countermeasures. In view of this, the proposed work introduces ICFD-31k, the first Indian Conversational Fraud Dataset, representing a new benchmark containing over 31,000 realistic conversational transcripts. ICFD-31k comprises systematically generated content, covering 10 distinct fraud umbrellas spanning from financial impersonation to job scams. ICFD-31k transcripts feature rich annotations comprising a final verdict, chunk-level streaming labels, and detailed ``slow-thinking'' rationales. In addition, the human-in-the-loop evaluation validates the ICFD-31k's quality, achieving a Cohen's Kappa of 0.534 that confirms annotation reliability. Furthermore, the proposed work introduces two fine-tuned models based on RoBERTa: M1 for non-streaming data and M2 for streaming data. The comprehensive experiments with strong baselines (M1, M2) further demonstrate the ICFD-31k's utility. The code and reproducibility materials are available at https://github.com/SPELLAILab/ICFD-31k.