One-third of financial institutions are accelerating their AI and machine learning (ML) adoption for anti-money laundering (AML) technology in response to COVID-19, an SAS study shows.
Also, another 39% of compliance professionals said their AI/ML adoption plans will continue unabated, despite the pandemic’s disruption, according to the study for which KPMG and the Association of Certified Anti-Money Laundering Specialists (ACAMS) collaborated.
The report, along with complementing survey data dashboard examine insights provided by more than 850 ACAMS members worldwide. They surveyed their employer organisations’ use of technology to detect money laundering, estimated in the range of 2% to 5% of global GDP – or US$800 billion to US$2 trillion – annually.
AI and ML have emerged as key technologies for compliance professionals as they look to streamline their AML compliance processes to fight financial crime and money laundering.
More than half (57%) of respondents have either deployed AI/ML into their AML compliance processes, are piloting AI solutions or plan to implement them 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% 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,” said Beer.
Further, 28% of 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.
“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 organizations,” said Tom Keegan, KPMG’s principal US solution leader for financial crimes and America forensic technology services.
The two primary drivers of AI and ML adoption, according to respondents, are to improve the quality of investigations and regulatory filings (40%); and to reduce false positives and resulting operational costs (38%).
David Stewart, director of financial crimes and compliance at SAS, said early adopters are gaining significant efficiencies while helping their institutions comply with rising regulatory expectations.
