Application of Machine Learning in Detection and Prevention of Money Laundering with Cryptocurrencies

Subject Areas : Computer Engineering and IT

Mojtaba Goodarzi ¹ , Mahdi Khaghani Esfahani ² , Mohammad Ali Kanani ³

1 - Department of Humanities, Criminal Law and Criminology, Kish International Branch, Islamic Azad University, Kish Island, Iran
2 - Department of Humanities, Criminal Law and Criminology, Research Institute for the Development of Humanities (SAMT), Tehran, Iran
3 - Department of Humanities, Criminal Law and Criminology, Roodhen Branch, Islamic Azad University, Tehran, Iran

Received: 2024-08-24 Accepted : 2024-11-20 Published : 2024-12-27

Keywords: machine learning, cryptocurrency transactions, money laundering, criminal policy.,

Abstract :

Money laundering, as one of the major challenges of financial systems, with the emergence of cryptocurrencies constantly breaks the boundaries of complexity and creates various types of crime. Cryptocurrencies have become an attractive tool for conducting illegal financial activities, including money laundering, due to their special features such as anonymity and the possibility of fast and cross-border transfers. "Machine learning" can be effectively used to identify suspicious patterns and prevent money laundering in the field of cryptocurrencies and decentralized financial systems. However, the success of this approach requires the formulation and implementation of a smart criminal policy that both exploits the opportunities arising from cryptocurrency technology and minimizes its risks. Using descriptive-analytical methods and content analysis, the article focuses on the legal, regulatory, and technical challenges associated with the use of machine learning in identifying money laundering in cryptocurrencies. Emphasizing the importance of international legal cooperation in formulating a smart criminal policy, machine learning models can play a vital role in strengthening the monitoring and prevention of financial crimes arising from/related to cryptocurrencies such as money laundering.

 

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