A third of financial institutions are accelerating their AI and machine learning (ML) adoption for anti-money laundering (AML) technology in response to COVID-19. Meanwhile, another 39% of compliance professionals said their AI/ML adoption plans will continue unabated, despite the pandemic’s disruption. These industry trends and others are explored in a new AML technology study by SAS, KPMG and the Association of Certified Anti-Money Laundering Specialists (ACAMS).
The report, Acceleration Through Adversity: The State of AI and Machine Learning Adoption in Anti-Money Laundering Compliance, and a complementing survey data dashboard examine insights provided by more than 850 ACAMS members worldwide. ACAMS surveyed each about their employer organizations’ 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 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.”
It’s not just the largest financial institutions leading the charge on technology adoption either. Twenty-eight percent 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 organizations label themselves industry leaders debunks the myth that advanced technological solutions beyond the reach of smaller financial organizations,” said Tom Keegan, Principal U.S. Solution Leader for Financial Crimes and America Forensic Technology Services, KPMG. “With both smaller and larger organizations 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 meet COVID-19’s disruption head on, while boosting accuracy and productivity, is the likely impetus to the industry’s accelerating use of advanced analytics for AML. The two primary drivers of AI and ML adoption, according to respondents, are to:
- Improve the quality of investigations and regulatory filings (40%).
- Reduce false positives and resulting operational costs (38%).
“The radical shift in consumer behavior 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 behavioral 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 programs quickly, with minimal disruption. Early adopters are gaining significant efficiencies while helping their institutions comply with rising regulatory expectations.”
For more insight into the state of AI and ML adoption in AML compliance, check out the on-demand AML webinar, The Truth Revealed: Global Insights on the Adoption of AI in the Fight Against Money Laundering and Financial Crime.