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Regulators are clamping down on banks that have inadequate technology and processes in place to spot money laundering activity, much of which is linked to terrorism and organised crime.
The criminal investigation by the Dutch public prosecution department was announced in a statement from the bank.
“The Dutch public prosecutor informed ABN Amro that the bank is the subject of an investigation relating to requirements under the act on the prevention of money laundering and financing of terrorism,” the statement said. “ABN Amro will cooperate fully with the investigation.”
Currently, only a small percentage of money laundering activity is spotted. With regulators clamping down on banks failing to prevent it, IT departments will play an important role in ensuring banks comply with strict regulation and avoid heavy fines. Tech startups are also building businesses around this problem.
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.
Bambos Tsiattalou, founding partner at specialist criminal and civil litigation firm Stokoe Partnership Solicitors, said: “ABN Amro’s failings to prevent money laundering over a long period of time highlights a systemic problem within the banking sector. Such organisations must adopt a more risk-based approach towards anti-money laundering, in particular how they conduct due diligence.”
According to the Dutch banking association, NVB, about €16bn of criminal money is circulating in the Netherlands, most of which is connected to the drugs trade.
Banks in the Netherlands – ABN Amro among them – recently announced a collaboration that will see them work together to improve their ability to spot suspicious transaction activity that could be linked to money laundering.
ING, Rabobank, Triodos Bank, ABN Amro and Volksbank are to set up an organisation that will monitor their combined transactions, with the aim of improving their chances of identifying and stopping money laundering.
The banks, which between them handle 9.8 billion payment transactions every year, plan to jointly set up a standalone operation that will combine huge volumes of transaction data with their vast IT expertise to focus on anti-money laundering (AML).
Read more about the battle against money laundering
- Danske Bank improves its anti-money laundering software, utilising artificial intelligence and machine learning.
- Financial services watchdogs inform banks that they must do more to improve anti-money laundering systems.
- Money laundering was back at the top of the agenda recently when the EU’s Fourth Anti-Money Laundering Directive came into force.
The group of banks involved could expand beyond five if the project is successful.
There are options for banks as startups focus on an area where regulations are getting tighter and the risk of heavy fines is forcing banks to invest in anti-money laundering products and services. Tech startup Comply Advantage provides on-demand cloud services to banks, via Amazon Web Services (AWS), including transaction monitoring.
Luke Mawbey, vice-president of engineering at the company, said financial services companies need to understand who they are doing business with. He said the company works with large banks across the world.
“The reason we exist is because the problem with money laundering and terrorism financing is so badly addressed. About $2tn is currently laundered every year – less than 3% is detected and a small part of this prevented,” said Mawbey. “This cannot be considered a success – it needs to be approached in a different way.”
The traditional way of collecting data to spot money laundering is intensely manual, including analysts reading articles and updating profiles, and doesn’t use modern computing techniques, said Mawbey.
A large proportion of the checks being made by human operators are based on false positives. This is very expensive with a very small success rate. Today, machine learning and natural language processing not only replaces the manual work, but far exceeds it in terms of capacity. Machines can read many more articles than humans and can automate anti-money laundering processes.