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

ArXi:2511.20944v4 Announce Type: replace 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.