Banks face a huge challenge in identifying and stopping money laundering without interfering with police investigations
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.
FCA investigation
But banks face a catch-22 situation, with the responsibility to report activity and help law enforcement investigate the criminals behind money laundering activities, but risk of fines if they let such activities through.
Ron Warmington, former global head of banking investigations at Citibank, said spotting suspicious transactions is the easiest part, but acting on it is difficult because of responsibilities that are almost impossible to balance.
Warmington has been in the position that many bankers face when they spot suspicious activity. “You are waiting for the authorities to respond and either say ‘stop the transaction now’ or ‘let it go’ or, as is usually the case, they say nothing,” he said.
That leaves a bank in a difficult situation because it is a criminal offence to tip off a money launderer that you are on to them, which you might do by blocking or delaying a transaction, said Warmington.
“Regulators want to be tipped off, but they also say that if you [unintentionally] tip off the crooks, they will come after you because you might be threatening a sting operation,” he added. “There is good technology to spot money laundering, but when you do spot it, how do you react to it?”
Tough decisions for banks
Linking systems with law enforcement agencies and implementing automatic blocks on certain transactions could help address the huge money laundering problem, according to David Bannister, analyst at Aite.
“Somehow link the databases of the law enforcement agencies and the banks,” he said. “Reports probably go to the regulator to collate, but if they were more proactive, it could tell them to block it.”
But Warmington added: “Generally, the technology available to the police and the regulators is not very good and they are well behind in the arms race. The question is, who is out there to design this stuff? You are talking serious investments in cash.”
Charlie Delingpole, CEO at fintech supplier Complyadvantage, which offers anti-money laundering automation technology and works with banks including Santander, Bank of America and challenger bank Oaknorth, said: “Technology and processes have moved on from what was acceptable in 2013, from when many of these revelations date. The regulators can’t ask the banks to do what is not technically achievable, but so much more is possible now.
“The regulators can set the bar far higher and money launderers should be far more worried.”
