Combating Money Laundering and Fraud in Blockchain Technology: A Regulatory Perspective and Machine Learning Based Approaches

Blockchain technology, with its characteristics such as decentralization and transparency, has brought about a significant transformation in the global financial system. However, these very features can also provide a suitable ground for criminal misuse, such as money laundering and fraud. This article, aiming to explore solutions for identifying and combating these threats, first analyzes regulatory and supervisory frameworks used in the United States, the European Union, international bodies such as the FATF, and several BRICS countries. Subsequently, in order to detect suspicious patterns, machine learning-based approaches such as decision trees, neural networks, and clustering algorithms have been employed. The analyzed data were extracted from blockchain networks and cryptocurrency transactions. The findings indicate that these methods can be highly effective in accurately identifying illegal activities. A case study of several real money laundering and fraud cases demonstrates the effectiveness of combining behavioral analysis, graph algorithms, and on-chain data. In the final section, leading blockchain analytics companies such as Chainalysis and Elliptic are introduced, and the need to enhance early detection tools, improve model accuracy, and integrate external data is emphasized. This research concludes that effective combat against financial crimes in the blockchain space requires the development of predictive models and globally coordinated regulatory frameworks to maintain public trust and the integrity of financial systems.

کلیدواژه ها:

Blockchain ، Money Laundering ، Cryptocurrency ، Financial Fraud ، Machine Learning