{"type":"ScholarlyArticle","title":"ICFD-31k: A Large-Scale Dataset and Benchmark for Real-Time Conversational Fraud Detection","authors":[{"name":"Rishi Ahuja","slug":"rishi-ahuja","given":"Rishi","family":"Ahuja","affiliation":""},{"name":"Kumar Prateek","slug":"kumar-prateek","given":"Kumar","family":"Prateek","affiliation":""},{"name":"Simranjit Singh","slug":"simranjit-singh","given":"Simranjit","family":"Singh","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2026/786","url":"https://ijcai.org/proceedings/2026/786","pdf":"https://ijcai.org/proceedings/2026/0786.pdf","markdown":"https://ijcai.org/proceedings/2026/786.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/786","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":786,"pages":{"first":7065,"last":7073},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Natural Language Processing: Natural Language Processing","Machine Learning: Machine Learning","AI Ethics, Trust, Fairness: AI Ethics, Trust, Fairness"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}