As part of their anti-money laundering (AMI) investments, financial services firms are increasing their use of artificial intelligence (AI) and machine learning technology to fight growing money laundering activity.
The UN estimates that up to $2 trillion is moved illegally each year. In the UK alone, the National Crime Agency estimates that money laundering costs the country’s economy £24 billion a year.
A recent study from KPMG, software company SAS and the Association of Certified Anti-Money Laundering Specialists, found a third of finance firms are increasing their use of AI and machine learning with the aim of combatting the growing problem.
The report, Acceleration through adversity: The state of AI and machine learning adoption in anti-money laundering compliance, surveyed 850 ACAMS members worldwide. It found that 57% of respondents have deployed artificial intelligence or machine learning in their anti-money laundering strategies or are testing new 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 of 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 primary reasons for financial services to adopt artificial intelligence and machine learning in their anti-money laundering strategies are to improve the quality of regulatory filings and investigations and to reduce false positive and resulting operational costs.
