How graph technology can map patterns to mitigate money-laundering risk

Abstract

The estimated amount of money laundered illegally worldwide is a staggering $800bn-$2tr. This equates to as much as 5% of global GDP, according to the United Nations Office on Drugs and Crime. 1 And the problem is growing thanks to digitisation, which has made it easier and faster to move money around. Money laundering is a growing problem. Criminals are getting more sophisticated, making eradicating this issue ever-more complex and challenging. The problem is that most tools for dealing with money laundering focus on discrete data. This makes it difficult to spot the shared characteristics that are typical of money-laundering networks. But graph technology has the power to mine the truth from data to help you quickly pinpoint potential areas of concern, explains Neo4j's Emil Eifrem.