Can banks solve money laundering puzzle through technology?

Recent revelations about huge volumes of money being laundered through big banks despite being spotted at the time of transaction should compel banks to develop anti-money laundering technology to do more than just flag up suspicious activity.

Connecting the databases of regulators, law enforcement and banks, and automatically blocking certain transactions, could be a way out of a banking catch-22 by enabling them to react more quickly. But the complex nature of stopping money laundering makes the tech challenge a big one.

Money laundering is currently headline news. Documents leaked to Buzzfeed News and shared with the International Consortium of Investigative Journalists revealed that banks including HSBC, Barclays and Standard Chartered have moved huge amounts of money despite spotting suspicious transactions.

Criminals use big banks to hide their dirty money, which is often linked to organised crime with funds being used to pay for assets to hide the money’s origin. According to the UN, about $2tn is moved illegally each year.

Headlines that big banks have allowed billions of pounds’ worth of money to be moved through them by criminals hit share prices hard and should trigger action, but banks are often stuck between a rock and a hard place. They have a responsibility to spot and stop suspicious activity and report it to regulators, but often they must also give law enforcement an opportunity to investigate first. Could technology be the answer?

While the money laundering figures are eye-watering, the detection rates are low, with human-intensive methods traditionally used to spot potential money laundering activity. Globally, only a low single-digit percentage of money laundering activity is detected and a small part of this is blocked.

Last year, the National Economic Crime Centre said money laundering costs the UK more than £100bn a year.

Banks, which potentially face heavy fines for failing to prevent such activity, have invested in systems to replace manual methods to spot money laundering as regulators clamp down on the criminal activity.

For example, in March this year, regulators in Sweden and Estonia imposed fines totalling €347m on Swedbank for breaching money laundering laws.

In the Netherlands, ING was fined €775m in 2018 after the regulator said the bank had failed to prevent the laundering of hundreds of millions of euros between 2010 and 2016.

And in 2017, Citigroup agreed to pay almost $100m and admitted to criminal violations as it settled an investigation into breaches of anti-money laundering rules involving money transfers between the US and Mexico. In the same year, Deutsche Bank was fined $650m by British and US authorities for allowing wealthy clients to move $10bn out of Russia.

Under the threat of huge financial penalties, banks have turned to technology to detect money laundering activity. Today, machine learning and natural language processing are being used to replace manual work. Machines can read many more articles than humans and can automate anti-money laundering processes.

Take HSBC, for example. In 2018, it announced it was using software from fintech startup Quantexa to help it automate the fight against money laundering, after a pilot the year before. The software analyses billions of data records, including internal and external sources, to spot potential money laundering activity. This will help HSBC to meet regulatory compliance and reduce risk for its business.