More than £100 billion a year is estimated to be laundered through the UK financial system, and UK financial institutions spend an estimated £5 billion a year on financial crime prevention. Most of this is spent on transaction monitoring systems, sanctions filters, Know-Your-Customer (KYC) and Customer Due Diligence (CDD) systems, and the recruitment and retention of compliance staff to detect suspicious activity. Firms are starting to leverage artificial intelligence and machine learning (AI/ML) to focus on transactions that present real risk by producing more effective alerts and reducing the volume needing human intervention. Advances in computing promise big gains for anti-money laundering (AML) compliance but are not always well understood. How does intelligent segmentation work and how can financial institutions incorporate AI/ML into their AML processes? In an article for Money Laundering Bulletin, Guidehouse's Managing Director James Siswick provides case studies on how AI/ML are already making a meaningful difference.
Special thanks to Dave Bradshaw, Johnny Zhang and James Robertson, who contributed to this article.