Banks rapidly adopting AI to detect and counter money laundering
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More than half of financial institutions have deployed AI and machine learning (ML) techniques to counter money-laundering attempts by cyber criminals, according to new research.
The study by SAS, KPMG and the Association of Certified Anti-Money Laundering Specialists (ACAMS), surveyed more than 850 ACAMS members worldwide about their use of technology to detect money laundering, which amounts to around 2 to 5 per cent of global GDP – or $800bn to $2tr – annually.
57 per cent of respondents said they had already deployed either AI or ML into their anti-money laundering compliance processes or are piloting solutions that they plan to implement in the next 12-18 months.
“As regulators across the world increasingly judge financial institutions’ compliance efforts based on the effectiveness of the intelligence they provide to law enforcement, it’s no surprise 66 per cent of respondents believe regulators want their institutions to leverage AI and machine learning,” said Kieran Beer, chief analyst at ACAMS.
“While many in the anti-financial-crime world – the regulators and financial institutions alike – are just coming up to speed on these advanced analytic technologies, there’s clearly shared hope that these tools will produce truly effective financial intelligence that catches the bad guys.”
The survey also found that while 28 per cent of financial institutions with assets greater than $1bn consider themselves fast adopters of AI technology, smaller firms were open to it too, with around 16 per cent of smaller financial institutions (those valued below $1bn) also viewing themselves as industry leaders in AI adoption.
“Seeing a strong percentage of smaller financial organisations label themselves industry leaders debunks the myth that advanced technological solutions beyond the reach of smaller financial organisations,” said Tom Keegan, who works in the Financial Crimes department at KPMG.
“With both smaller and larger organisations subject to the same level of regulatory scrutiny, it’s important that these numbers continue to rise.”
Regardless of institution size, the pressure on banks to boost their accuracy and productivity at detecting money laundering while tackling Covid-19 related challenges is the likely impetus behind the accelerating use of AI and ML.
“The radical shift in consumer behaviour sparked by the pandemic has forced many financial institutions to see that static, rules-based monitoring strategies simply aren’t as accurate or adaptive as behavioural decisioning systems,” said David Stewart, director of financial crimes and compliance at SAS.
“AI and ML technologies are dynamic by nature, able to intelligently adapt to market changes and emerging risks – and they can be integrated into existing compliance programmes quickly, with minimal disruption. Early adopters are gaining significant efficiencies while helping their institutions comply with rising regulatory expectations.”
The increasing use of cryptocurrencies in recent years – partly driven by a spike in their value – has made it easier than ever for criminals to launder illicit funds.
Last month, the European Commission proposed that cryptocurrency transactions should be made traceable in order to help authorities investigate money laundering. The proposals would effectively expand financial rules governing traditional financial service providers to cover cryptocurrency service providers as well.
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