Prevention and Prediction of Cryptocurrency Fraud Detection Using Machine Learning

Abstract

This article talks about the growing problems that law enforcement faces because of the possibility that Bitcoin could be used for money laundering and funding terrorism.  Using unsupervised machine learning, we suggest a new way to look at the whole Bitcoin user graph, which will help find suspicious people who are breaking the law.  The paper's findings are very promising for improving the detection of cryptocurrency crimes, which will help fight money laundering and terrorism financing.  Our main goal is to look at the whole Bitcoin user graph using a Coin Join community detection method.  By studying transaction patterns and network interactions among these communities, our technique tries to detect and flag questionable people involved in criminal activities.   The conclusions of our research provide enormous promise for boosting anti-money laundering efforts and strengthening counterterrorism measures through more effective bitcoin crime detection tools.   We want to make a big difference in the ongoing efforts to protect financial systems and fight illegal financial activities by giving law enforcement agencies better tools and information.