用多视角集成学习提升夜间存款诈骗识别能力
ChequeMark: An Ensemble Machine Learning Framework for After-Hours Business Deposit Fraud Detection
- 融合梯度提升、异常检测与图神经网络，从行为、异常、关系三方面综合判断风险
- 在分布偏移场景下F1达83.77%，误报率仅0.69%，优于单一模型
- 输出可解释的评分，适合金融风控团队快速决策
支票欺诈是夜间企业存款业务中的重大风险，因资金可在一天内释放，而支票清算需数日，形成欺诈暴露窗口。现有缓解手段依赖静态的单笔存款检查，难以捕捉客户历史行为与动态模式。为此，我们提出一种多视图集成机器学习框架，结合极端梯度提升（XGBoost）识别已知欺诈模式、孤立森林进行无标签异常检测、图采样与聚合（GraphSAGE）挖掘交易关联模式，并将三者输出整合为客户端风险评分。在稳定条件下性能与XGBoost相当；在目标分布偏移下，本框架表现最佳（F1: 83.77%，FPR: 0.69%），显著优于XGBoost（F1: 82.77%，FPR: 0.72%）。结果表明该框架具备更强的鲁棒性，同时通过基于行为、异常与关系证据的自然语言解释保持可解释性。
原文摘要 · Abstract (English)
Cheque fraud is a material risk in after-hours business deposit operations because funds may be released within one business day, while cheque clearing takes several days. This timing gap creates a fraud exposure window for financial institutions. Prior mitigation relies on static, deposit-level checks and therefore miss historical client behavior and evolving patterns. To address this gap, we propose a multi-view ensemble ML framework that combines: Extreme Gradient Boosting (XGBoost) for known fraud patterns, Isolation Forest for label-free anomaly detection, and Graph Sample and Aggregate (GraphSAGE) for relational patterns associated with transaction activities. We then combine the three outputs into a single client-level risk score. Under stable conditions, performance is comparable to XGBoost; under a targeted distribution shift, our framework performs best (F1: 83.77%, FPR: 0.69%) versus XGBoost (F1: 82.77%, FPR: 0.72%). These results indicate improved robustness to distribution shift while preserving interpretability through plain-language explanations grounded in behavioural, anomaly, and relational evidence.
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