ChequeMark: An Ensemble Machine Learning Framework for After-Hours Business Deposit Fraud Detection

Abstract: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.

Machine Learning (cs.LG)

arXiv:2608.21629 [cs.LG]

(or arXiv:2608.21629v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.21629 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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