{"ID":6267520,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-11T11:26:19.944767982Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07751","arxiv_id":"2607.07751","title":"Forensic Schema for Psychological Manipulation in Cyber Fraud: LLM-Driven Victim Reports Analysis","abstract":"Existing cybercrime classification schemas capture contact metadata and financial transactions but omit the psychological manipulation techniques perpetrators employ. We present a forensic schema (four categories, 35 questions) adding 11 manipulation indicators and cryptocurrency evidence fields to established forensic foundations. Applied to 10,994 victim reports via large language model (LLM)-driven annotation and validated against two human annotators (mean LLM-human $κ= 0.69$, matching inter-annotator $κ= 0.68$), the schema revealed a statistically distinct manipulation profile for each major fraud type (Cramer's $V$ up to $0.790$). A rationale-based evidence audit nonetheless exposed a forensic detail gap: detection of manipulation techniques was reliable, but victim narratives varied widely in the actionable detail supporting each Yes answer, and blockchain-specific identifiers were nearly absent. These findings point to AI-assisted victim intake with schema-informed follow-up questions as the most direct way to close the gap. The tiered annotation strategy also provides a reusable template for LLM-based extraction from other forensic text domains.","short_abstract":"Existing cybercrime classification schemas capture contact metadata and financial transactions but omit the psychological manipulation techniques perpetrators employ. We present a forensic schema (four categories, 35 questions) adding 11 manipulation indicators and cryptocurrency evidence fields to established forensic...","url_abs":"https://arxiv.org/abs/2607.07751","url_pdf":"https://arxiv.org/pdf/2607.07751v1","authors":"[\"Zikai Alex Wen\",\"Corrazon Ogot\",\"Juan Li\",\"Yan Bai\"]","published":"2026-07-08T11:37:56Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}