AMLgentex: Mobilizing Data-Driven Research to Combat Money Laundering
Current AML research is hindered by the scarcity of real-world transaction data and the failure of existing synthetic datasets to capture critical characteristics—including partial observability, temporal dynamics, strategic actor behavior, label uncertainty, class imbalance, and network dependencies. To address these limitations, we propose AMLgentex, an open-source framework that— for the first time—systematically models money laundering as a strategic, partially observable process with multi-scale network dependencies. It enables configurable, high-fidelity generation of spatiotemporal transaction graphs with uncertainty-aware labels. Our approach integrates graph neural networks, stochastic processes, and game-theoretic behavioral modeling, augmented by adversarial label injection. Extensive evaluation across multiple benchmark detection models demonstrates that AMLgentex significantly enhances robustness assessment under low signal-to-noise ratios and cross-institutional settings. The framework is publicly released and has been widely adopted by the financial compliance community.