AI gains momentum in war against money-laundering
A third of surveyed financial institutions across the globe are accelerating their artificial intelligence (AI) and machine learning (ML) technology for anti-money-laundering (AML) purposes, in response to COVID-19.
This is according to a new AML technology study conducted by software firm SAS, in collaboration with consultancy KPMG and the Association of Certified Anti-Money-Laundering Specialists (ACAMS).
The report, titled “Acceleration through adversity: The state of AI and machine learning adoption in anti-money-laundering compliance”, examines insights provided by more than 850 ACAMS financial institution members worldwide.
ACAMS surveyed each respondent about their employer organisation’s use of technology to detect money-laundering, estimated to account in the range of 2% to 5% of the global gross domestic product – or $800 billion to $2 trillion – annually.
According to the study, AI and ML have emerged as key technologies for financial institutionsas they look to streamline their AML compliance processes to fight financial crime and money-laundering, exacerbated by a COVID-driven surge in cyber fraud.
More than half (57%) of surveyed respondents have either deployed AI/ML in their AML compliance processes, are piloting AI solutions, or plan to implement them in the next 12 to 18 months, it notes.
Meanwhile, another 39% of compliance professionals said their AI/ML adoption plans will continue unabated, despite the pandemic’s disruption.
“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% of respondents believe regulators want their institutions to leverage AI and machine learning,” says Kieran Beer, chief analyst and director of editorial content 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.”
According to a report by Grand View Research, the global anti-money-laundering market size was valued at $1.03 billion in 2020. It is expected to expand at a compound annual growth rate of 15.6% from 2021 to 2028, attributed to the increase in money-laundering-combating tech solutions across the globe.
The SAS study points out AI and machine learning are gaining serious momentum in AML compliance, as they help reduce false positives, ease caseloads, streamline reporting and lower operational costs. And yet, some have been slow to change despite the promising results and billions spent annually on basic regulatory compliance tasks.
The primary drivers of AI and machine learning adoption, according to respondents, are to improve the quality of investigations and regulatory filings (40%), and reduce false positives and resulting operational costs (38%).
“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,” notes David Stewart, director of financial crimes and compliance at SAS.
“AI and machine learning 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.”
It’s not just the largest financial institutions leading the charge on technology adoption either.
Some 28% of surveyed large financial institutions − those with assets greater than $1 billion − consider themselves innovators and fast adopters of AI technology. However, encouragingly, 16% of smaller financial institutions (those valued below $1 billion) also view themselves as industry leaders in AI adoption, SAS notes.
“Seeing a strong percentage of smaller financial organisations label themselves as industry leaders debunks the myth that advanced technological solutions are beyond the reach of smaller financial organisations,” says Tom Keegan, KPMG principal US solution leader for financial crimes and America forensic technology services.
“With both smaller and larger organisations subject to the same level of regulatory scrutiny, it’s important that these numbers continue to rise.”