Machine learning techniques for Money Laundering suspicious operations classification

Money laundering and Financing of Terrorism is the process that converts money illegally obtained, into money apparently obtained from legitimate sources. In the last decades the scope and importance of this crime has raised exponentially due to the appearance of new technologies, several international and national associations has been formed to help to detect the crime and fight it. The usual method to detect it has slightly evolved in the past years, is a very manual process in which the Anti-money Laundering technicians have to research and review a lot of financial operations and profiles wasting loads of time and money, usually helped by rule-based detection systems that help them to find the suspicious operations and profiles. To avoid this wasting in resources, this study will address the Money-Laundering Detection problem by implementing several Machine Learning techniques to correctly classify the suspicious operations/profiles