Third of finance firms accelerate use of artificial intelligence to detect money laundering

Finance firms are stepping up their investments in artificial intelligence (AI) and machine learning (ML) as part of their anti-money laundering (AML) investments.

Covid-19 and the disruption it brought to the global economy has triggered a sudden increase in financial crime, with money laundering a threat to society.

The UN estimates that up to $2tn is moved illegally each year. Criminals use big banks to hide money, which is often linked to organised crime, with funds being used to pay for assets to hide the money’s origin. In the UK, the National Crime Agency (NCA) estimates that money laundering costs the country’s economy £24bn each year.

According to a study from KPMG, software company SAS and the Association of Certified Anti-Money Laundering Specialists (ACAMS), a third of finance firms are accelerating the use of AI and ML in their AML strategies to fight the growing problem.

The study report, Acceleration through adversity: The state of AI and machine learning adoption in anti-money laundering compliance, questioned 850 ACAMS members worldwide.

Over half (57%) of respondents have either deployed AI or ML into their AML compliance processes, or are piloting AI solutions or plan to implement them within 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.”

The two main reasons for adopting AI and ML in AML processes are to improve the quality of investigations and regulatory filings, cited as the main reason by (40%) and to reduce false positives and resulting operational costs, according to 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,” 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.”

Banks that have fallen short in their AML strategies have been hit by huge fines from regulators in recent years. According to research published in February 2021 by business-to-business (B2B) information services company Kyckr, 28 financial institutions across the globe were fined for AML related violations in 2020, equating to roughly £2.6bn. In March that year, regulators in Sweden and Estonia imposed fines worth €347m on Swedbank for breaching money laundering laws.

In the Netherlands, ING was fined €775m in 2018, after the regulator said the bank had failed to prevent the laundering of hundreds of millions of euros between 2010 and 2016.

In 2017, Citigroup agreed to pay almost $100m and admitted to criminal violations as it settled an investigation into breaches of anti-money laundering rules involving money transfers between the US and Mexico. In the same year, Deutsche Bank was fined $650m by British and US authorities for allowing wealthy clients to move $10bn out of Russia.