Intelligent regulation for cryptocurrency anti-money laundering: models, methods, and applications

In recent years, blockchain-enabled cryptocurrency market has been rapidly evolving to a trillion-dollar economic system with various kinds of emerging business models and application scenarios. Cryptocurrencies, characterized by decentralization, anonymity, and ease of cross-border circulation, have significantly enriched and evolved the mainstream financial system centered around fiat currencies. However, they are also extensively used in illegal financial activities including money laundering. As such, research on anti-money laundering (AML) regulation for cryptocurrencies has become a burgeoning field of interest, with machine learning and artificial intelligence (AI) emerging as primary methodologies in areas such as cryptocurrency transaction monitoring, transaction network analysis, and security risk assessment. First, the recent research progress on intelligent AML regulation for cryptocurrencies was systematically reviewed. The main models, methods, and application patterns in AML regulatory research were summarized from multiple dimensions, including traditional machine learning, deep learning, ensemble learning, graph analysis, and heuristic approaches. Meanwhile, commonly used datasets in cryptocurrency AML research were compiled, and a performance comparison of existing models and algorithms was provided based on the Elliptic dataset. Finally, the research challenges and future directions of cryptocurrency AML were discussed, aiming to offer valuable references and insights for the prosperity and healthy development of the cryptocurrency and decentralized finance industry.