发表机构
Royal Bank of Canada（加拿大皇家银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非工作时段商业存款的支票欺诈风险，ChequeMark多视图集成ML框架结合XGBoost、Isolation Forest与GraphSAGE生成客户风险评分，在分布偏移场景下性能优于单一模型，提升了鲁棒性与可解释性。
AI 中文摘要
支票欺诈是非工作时段商业存款业务中的重大风险，因为资金可能在一个工作日内释放，而支票清算需要数天时间。这种时间差为金融机构创造了欺诈暴露窗口。以往的缓解措施依赖于静态的、存款层面的检查，因此忽略了历史客户行为和不断演变的模式。为解决这一差距，我们提出一种多视图集成机器学习框架，该框架结合了：用于已知欺诈模式的极端梯度提升（XGBoost）、用于无标签异常检测的孤立森林（Isolation Forest），以及用于与交易活动相关的关系模式的图采样与聚合（GraphSAGE）。随后我们将三个输出合并为单一的客户层面风险评分。在稳定条件下，性能与XGBoost相当；在目标分布偏移下，我们的框架表现最佳（F1：83.77%，FPR：0.69%），而XGBoost的F1为82.77%、FPR为0.72%。这些结果表明，该框架在保持基于行为、异常和关系证据的通俗语言解释可解释性的同时，提升了对分布偏移的鲁棒性。
英文摘要
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.
