发表机构
OCBC, Singapore（新加坡华侨银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对洗钱者账户检测难题，提出端到端管道，含LightGBM分类器、TreeSHAP归因层和LLM模块。经评估，系统收益率提升，警报量增加，LLM叙述减少认知负荷，在实际部署中表现出色，还探讨了在监管金融环境中的应用影响。
AI 中文摘要
洗钱者账户是金融欺诈的关键促成因素，但由于交易和行为数据的异质性，大规模检测它们仍然具有挑战性。我们提出了一个用于客户级洗钱者检测的端到端管道，包括三个阶段：一是基于280个工程特征训练的LightGBM分类器；二是将每个预测分解为特征贡献的TreeSHAP归因层；三是将SHAP归因转换为面向分析师的自然语言叙述的大语言模型（LLM）模块。我们在三个开放权重的LLM系列上进行评估，并通过分析师反馈评估解释质量。在实际生产部署中，该系统的收益率从现有基于规则系统的61%提高到89%，每月警报量从211增加到302，定性反馈表明LLM生成的叙述减少了警报分类期间的认知负荷。我们还讨论了在受监管的金融环境中部署LLM增强的可解释性的影响。
英文摘要
Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data. We present an end-to-end pipeline for customer-level mule detection comprising three stages: (1) a LightGBM classifier trained on 280 engineered features spanning transaction patterns, account demographics, network topology, and temporal behaviour; (2) a TreeSHAP attribution layer that decomposes each prediction into feature contributions; and (3) a large language model (LLM) module that converts SHAP attributions into analyst-facing natural-language narratives. We evaluate across three open-weight LLM families and assess explanation quality through analyst feedback. In a live production deployment, the system achieves a yield rate of 89%, up from 61% under the incumbent rule-based system, with monthly alert volume expanding from 211 to 302, reflecting broader true-positive coverage rather than increased noise. This corresponds to a 60% incremental adverse detection beyond existing review workflows, substantially outperforming the rule-based approach. Qualitative feedback from analysts indicates that LLM-generated narratives reduce cognitive load during alert triage. We further discuss implications of deploying LLM-augmented explainability in regulated financial environments.
