Different Techniques And Algorithms To Combat The Issue Of Money Laundering In Bitcoin

Abstract

The emergence of Bitcoin has continued to grow both in value and fame, as it was introduced as the first decentralized cryptocurrency. Many studies have shown that criminals are exploiting bitcoin by using it to launder their money, which originates from illegal activities or cybercrime. This paper aims at providing a comparison of detection techniques for preventing money laundering in bitcoin through various methods including graph theory, prevention of mixing services, and machine learning techniques including Random Forest, Shallow Neural Networks, optimizable Decision Trees, Bagging, Boosting algorithms, and Ensemble learning combining Random Forest, Bagging and Extra Tree.