SAS Study Shows a COVID-driven Surge in Fraud and Financial Crime

 Pandemic spurs banks' AI adoption for AML, SAS study shows
Amid a COVID-driven surge in fraud and financial crime, quite half financial institutions have either already deployed AI in their anti-money laundering compliance processes or decide to imminently

A third of monetary 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 et al. are explored during a new AML technology study by SAS, KPMG and therefore 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 quite 850 ACAMS members worldwide. ACAMS surveyed each about their employer organizations’ use of technology to detect concealment, estimated within the range of twenty-two to five of worldwide GDP – or US$800 billion to US$2 trillion – annually.

AI and ML have emerged as key technologies for compliance professionals as they appear to streamline their AML compliance processes to fight financial crime and concealment. quite half (57%) of respondents have either deployed AI/ML into their AML compliance processes, are piloting AI solutions or decide to implement them within the next 12-18 months.

“As regulators across the planet increasingly judge financial institutions’ compliance efforts supported the effectiveness of the intelligence they supply to 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 within the anti-financial crime world – the regulators and financial institutions alike – are just arising to hurry 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 most important financial institutions leading the charge on technology adoption either. Twenty-eight percent of huge 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 robust percentage of smaller financial organizations label themselves industry leaders debunks the parable 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 bigger organizations subject to an equivalent level of regulatory scrutiny, it’s important that these numbers still rise.”

Regardless of institution size, the pressure on banks to satisfy COVID-19’s disruption head on, while boosting accuracy and productivity, is that the likely impetus to the industry’s accelerating use of advanced analytics for AML. the 2 primary drivers of AI and ML adoption, consistent with respondents, are to:

•     Improve the standard 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 ascertain that static, rules-based monitoring strategies simply aren’t as accurate or adaptive as behavioral decisioning systems,” said David Stewart, Director of monetary Crimes and Compliance at SAS. “AI and ML technologies are dynamic naturally, ready to intelligently adapt to plug changes and emerging risks – and that they are often integrated into existing compliance programs quickly, with minimal disruption. Early adopters are gaining significant efficiencies while helping their institutions suits rising regulatory expectations.”