AMLgentex: Mobilizing Data-Driven Research to Combat Money Laundering

2025-06-03

arXiv.org

AI Summary

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.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Generation
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Money laundering enables organized crime by allowing illicit funds to enter the legitimate economy. Although trillions of dollars are laundered each year, only a small fraction is ever uncovered. This stems from a range of factors, including deliberate evasion by launderers, the rarity of confirmed cases, and the limited visibility each financial institution has into the global transaction network. While several synthetic datasets are available, they fail to model the structural and behavioral complexity of real-world money laundering. In particular, they often overlook partial observability, sparse and uncertain labels, strategic behavior, temporal dynamics, class imbalance, and network-level dependencies. To address these limitations, we present AMLGentex, an open-source suite for generating realistic, configurable transaction data and benchmarking detection methods. It enables systematic evaluation of anti-money laundering (AML) systems in a controlled environment that captures key real-world challenges. We demonstrate how the framework can be used to rigorously evaluate methods under conditions that reflect the complexity of practical AML scenarios.

Problem

• Low detection rates for money laundering despite trillions laundered annually
• Restricted access to real transaction data hinders detection method development
• Existing synthetic datasets lack realistic money laundering scenario characteristics

Innovation

• Generates realistic configurable transaction datasets
• Enables systematic evaluation under real-world conditions
• Provides open-source benchmarking suite for AML detection