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Reality of spotting money laundering

Most, 74 per cent of business customers think banks use machine learning and artificial intelligence to spot money laundering. In reality banks rely on human investigators to manually sift through alerts – as selected only by 31pc of respondents, in a survey by Vanson Bourne for the defence and cyber product company BAE Systems. This lack of automation and modern processes is having a major impact on efficiency and expense in countering money laundering, BAE says.

Brian Ferro, Global Compliance Solutions Product Manager at BAE Systems Applied Intelligence, said: “Compliance investigators at banks can spend up to three days of their working week dealing with alerts – which most of the time are false positives. By occupying key personnel with these manual tasks, banks are limiting the investigators’ role, impacting on their ability to stop criminal activity.”

Money laundering is known to fund and enable slavery, drug trafficking, terrorism, corruption and organised crime. Three quarters (75pc) of business customers surveyed see banks as central actors in the fight against money laundering. The penalty for failing to stop money laundering can be high for banks – and is not restricted to significant fines. When questioned, one in four, 26pc of survey respondents said they would move their business’ banking away from a bank that had been found guilty and fined for serious and sustained money laundering that it had not identified.

Ferro added: “For banks to be on the front foot against money laundering, their investigators need to be supported by machine intelligence. Simplifying, optimising and automating the sorting of these alerts to give human investigators more time is the single most valuable thing banks and the compliance industry can do in the fight against money launderers. Right now, small improvements in efficiency of the systems banks use to find laundering can yield huge results. At BAE Systems we use a combination of intelligence-led advanced analytics to track criminals through the world’s financial networks. By putting machine learning and artificial intelligence systems to work to narrow down the number of alerts, human investigators can concentrate on tasks more suited to their talents and insight.”

About the survey

Interviews were online in February 2018, from 300 IT decision makers in the UK and the US, from organisations with 1000 employees or more, in a variety of commercial sectors.


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