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 baselines (TF-IDF+LogReg and character n-gram+Linear SVM), an ablation study for the Smiling Assassin Score, and a homoglyph-map sensitivity analysis. DistilBERT achieves AUC = 1.0000 and F1 = 0.9981 at 7.403 ms per email on GPU; CatBoost achieves AUC = 0.9860 and F1 = 0.9382 at 0.855 ms on CPU. A three-way cost-sensitive decision policy (auto-allow, auto-block, manual review) optimises expected financial loss under a 1:5,167 false-negative-to-false-positive cost ratio.

翻译：商务邮件欺诈（BEC）是一种高影响力的社交工程威胁，具有极端的操作不对称性：漏报可能导致巨额经济损失，而误报主要产生调查和延迟成本。本文在成本敏感的决策框架下比较了两种BEC检测范式：（i）基于语义的Transformer方法（DistilBERT）进行上下文语言理解，（ii）采用工程化语言和结构线索的取证心理语言学方法（CatBoost）。我们在一个混合数据集（样本量N=7,990）上对两者进行评估，该数据集结合了合法企业邮件和跨越30种BEC分类学（包括字符级Unicode混淆）的AI合成对抗欺诈文本。我们添加了经典基线方法（TF-IDF+逻辑回归和字符n-gram+线性支持向量机）、“微笑刺客”得分的消融实验以及同形字映射敏感性分析。DistilBERT在GPU上达到AUC=1.0000和F1=0.9981，每封邮件处理时间7.403毫秒；CatBoost在CPU上达到AUC=0.9860和F1=0.9382，每封邮件处理时间0.855毫秒。一种三向成本敏感决策策略（自动放行、自动拦截、人工审核）在1:5,167的漏报-误报成本比率下优化了预期财务损失。