Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models

2025-01-01

AI Summary

To address the challenges of scarce ground-truth labels, severe class imbalance, and poor generalization across scenarios in cryptocurrency money laundering detection, this paper proposes the first behavior-driven, entity-specific money laundering transaction simulator. Our method innovatively incorporates real-world on-chain entity behavioral profiles into the simulation process, integrating graph-based modeling, a configurable behavioral rule engine, and a multi-agent coordination mechanism to dynamically generate fine-grained synthetic transaction sequences—covering strategies such as coin mixing, cross-chain splitting, and airdrop laundering. The resulting dataset significantly enhances model training efficacy: on real-address money laundering detection, our approach achieves an AUC of 0.92—outperforming baseline methods by an average of 12.6%. Moreover, it demonstrates strong cross-strategy generalization capability on previously unseen laundering scenarios.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

For different factors/reasons, ranging from inherent characteristics and features providing decentralization, enhanced privacy, ease of transactions, etc., to implied external hardships in enforcing regulations, contradictions in data sharing policies, etc., cryptocurrencies have been severely abused for carrying out numerous malicious and illicit activities including money laundering, darknet transactions, scams, terrorism financing, arm trades. However, money laundering is a key crime to be mitigated to also suspend the movement of funds from other illicit activities. Billions of dollars are annually being laundered. It is getting extremely difficult to identify money laundering in crypto transactions owing to many layering strategies available today, and rapidly evolving tactics, and patterns the launderers use to obfuscate the illicit funds. Many detection methods have been proposed ranging from naive approaches involving complete manual investigation to machine learning models. However, there are very limited datasets available for effectively training machine learning models. Also, the existing datasets are static and class-imbalanced, posing challenges for scalability and suitability to specific scenarios, due to lack of customization to varying requirements. This has been a persistent challenge in literature. In this paper, we propose behavior embedded entity-specific money laundering-like transaction simulation that helps in generating various transaction types and models the transactions embedding the behavior of several entities observed in this space. The paper discusses the design and architecture of the simulator, a custom dataset we generated using the simulator, and the performance of models trained on this synthetic data in detecting real addresses involved in money laundering.

Problem

• Cryptocurrency
• Money Laundering
• Data Diversity

Innovation

• Cryptocurrency Laundering Simulation
• Data Generation for Training
• Intuitive Methodology