[2604.02899] Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation

Computer Science > Machine Learning arXiv:2604.02899 (cs) [Submitted on 3 Apr 2026] Title:Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation Authors:Haseeb Tariq, Marwan Hassani View a PDF of the paper titled Extracting Money Laundering Transactions from Quasi-Temporal Graph Representation, by Haseeb Tariq and Marwan Hassani View PDF HTML (experimental) Abstract:Money laundering presents a persistent challenge for financial institutions worldwide, while criminal organizations constantly evolve their tactics to bypass detection systems. Traditional anti-money laundering approaches mainly rely on predefined risk-based rules, leading to resource-intensive investigations and high numbers of false positive alerts. In order to restrict operational costs from exploding, while billions of transactions are being processed every day, financial institutions are investing in more sophisticated mechanisms to improve existing systems. In this paper, we present ExSTraQt (EXtract Suspicious TRAnsactions from Quasi-Temporal graph representation), an advanced supervised learning approach to detect money laundering (or suspicious) transactions in financial datasets. Our proposed framework excels in performance, when compared to the state-of-the-art AML (Anti Money Laundering) detection models. The key strengths of our framework are sheer simplicity, in terms of design and number of parameters; and scalability, in terms of the computing and memory requirements. ...