A Framework to Build User Profile on Cryptocurrency Data for Detection of Money Laundering Activities

Abstract

Ever since Bitcoin got its popularity, cryptocurrencies becomes the first choice of investors, tech enthusiasts, and criminals. Nowadays it became a prominent tool for money laundering, hawala and criminal payment system. Money laundering is the process of converting illegal black money to legal white money. This can be done by various ways like transferring money from one account to another via multiple intermediate accounts. Detecting this complex activity is very difficult and challenging, because of the high volume of transactions and bitcoins data structure. There are many methods proposed in different papers to detect money laundering in the conventional banking system. In this study, we proposed a framework to convert the bitcoin's transnational data into a similar data frame of bank's user database, which is used by some existing state of the art intelligent systems to detect the abnormal cluster of transactions and user behavior.