Review of the Principal Indicators and Data Science Techniques Used for the Detection of Financial Fraud and Money Laundering

Abstract

Financial fraud and money laundering represent an important concern both nationally and worldwide due to the huge amounts of monetary losses they imply. Besides human inspection, Data Science (DS) has proved so far to be a useful tool in order to fight these activities by automatic means. Nevertheless, this approach to the problem is still in early stages, thus yielding plenty of field to be explored. This document discusses some of the principal variables/indicators and DS techniques that have been used in the recent years for detecting and describing fraudulent operations, to serve as a guide for future work in the field.