How AML Is Evolving To Catch Bad Acts Before They Happen

Blog entry

Mention financial crime prevention and your mind probably jumps to the latest machine learning and artificial intelligence solutions for transaction monitoring, fraud prevention, autonomous monitoring and so on. But these solutions, while essential and invaluable, only address part of the problem – they are reactive rather than proactive, catching issues only after the fact.

This is a fundamental challenge at the heart of money laundering, terrorist financing and other forms of financial crime, but there are answers. Ed Sander, president of Arachnys, a New York-based firm focusing on customer risk intelligence solutions for Know Your Customer (KYC), anti-money laundering (AML) and enhanced due diligence (EDD), says: “Previous solutions have focused primarily on spotting transactions that have already occurred or are in flight. Unlike fraud, money laundering is very hard to follow in real time, which means that most AML solutions track events that have already happened.”

However, a new movement is evolving – one that focuses on entities that could perform the criminal transactions rather than the transactions themselves, with the goal of catching money laundering activity before it even happens.

“If you know enough about a known bad actor, then you can identify unknown bad actors through matching behaviour patterns,” explains Sander. “If you can do this, then you have the potential to stop events before they happen.” In essence, this is a form of profiling, through which analysts can track, flag, and in theory, prevent risk before it enters the bank.

While a relatively new area, this intelligence-led approach is already gaining traction. In April 2018 HSBC announced plans to integrate new technology from Quantexa, a UK-based big data analytics company, to combat financial crime by analysing internal, publicly available and transactional data within a customer’s wider network to stop potential money laundering before it happens.

The deployment of the technology follows a pilot of the software at HSBC in 2017 and will see the global bank and data start-up work together to better detect potentially illegal activity in its broader context, helping the bank fulfil its regulatory responsibilities and provide better understanding of the overall risk.

Quantexa CEO Vishal Marria says: “We will be supporting the bank to join the dots of all their data to give a broader understanding of their customers and transactions across the globe. Through a better understanding, HSBC will be better equipped in its fight against financial crime.”

Arachnys too has joined the movement, with the recent development of cloud-based Entity Management solutions (soft launched in August) that leverage machine learning and work with banks to help them spot potential issues in advance. 

“It is a whole new world, with a paradigm shift occurring in the industry,” says Sander. “Of course you need the old technologies – the regulators still require solutions to monitor transactions. But a lot of banks, around the world, are investing significant sums into entity profiling capabilities, and this area of AML and financial crime prevention is likely to grow very quickly.”