AMLgentex: Mobilizing Data-Driven Research to Combat Money Laundering
This work addresses the challenge of developing effective anti-money laundering systems for financial institutions and regulators by providing a tool to overcome data access restrictions and improve detection methods, though it is incremental as it builds on existing synthetic data approaches.
The authors tackled the problem of low detection rates in anti-money laundering by introducing AMLGentex, an open-source suite for generating realistic, configurable transaction data and benchmarking detection methods, which includes multiple country-specific datasets and parameter guidance to empower researchers and practitioners.
Money laundering enables organized crime by moving illicit funds into the legitimate economy. Although trillions of dollars are laundered each year, detection rates remain low because launderers evade oversight, confirmed cases are rare, and institutions see only fragments of the global transaction network. Since access to real transaction data is tightly restricted, synthetic datasets are essential for developing and evaluating detection methods. However, existing datasets fall short: they often neglect partial observability, temporal dynamics, strategic behavior, uncertain labels, class imbalance, and network-level dependencies. We introduce AMLGentex, an open-source suite for generating realistic, configurable transaction data and benchmarking detection methods. AMLGentex enables systematic evaluation of anti-money laundering systems under conditions that mirror real-world challenges. By releasing multiple country-specific datasets and practical parameter guidance, we aim to empower researchers and practitioners and provide a common foundation for collaboration and progress in combating money laundering.