[2511.20944] Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection

Computer Science > Machine Learning arXiv:2511.20944 (cs) [Submitted on 26 Nov 2025 (v1), last revised 5 Apr 2026 (this version, v4)] Title:Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Authors:Yaw Osei Adjei (Kwame Nkrumah University of Science and Technology, Kumasi, Ghana), Frederick Ayivor (Independent Researcher, Fishers, Indiana, USA) View a PDF of the paper titled Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection, by Yaw Osei Adjei (Kwame Nkrumah University of Science and Technology and 5 other authors View PDF HTML (experimental) Abstract:Business Email Compromise (BEC) is a high-impact social engineering threat with extreme operational asymmetry: false negatives can trigger large financial losses, while false positives primarily incur investigation and delay costs. This paper compares two BEC detection paradigms under a cost-sensitive decision framework: (i) a semantic transformer approach (DistilBERT) for contextual language understanding, and (ii) a forensic psycholinguistic approach (CatBoost) using engineered linguistic and structural cues. We evaluate both on a hybrid dataset (N = 7,990) combining legitimate corporate email and AI-synthesised adversarial fraud generated across 30 BEC taxonomies, including character-level Unicode obfuscations. We add classical bas...