In recent years enforcement actions related to AML have been on the rise. Regulatory authorities have become more vigilant in the battle against money laundering and terrorist financing, with regulators levying $36 billion in fines worldwide.

The growing importance of AML compliance and increased sanctions, coupled with the challenging labor market, overworked employees, and ongoing recession fears, creates several challenges for financial institutions:

• Rising costs: AML teams’ staffing and training or outsourcing cost large institutions hundreds of millions annually and create significant cost pressure for institutions.
• Operational inefficiency: Analysts spend most of their time performing nonanalytical work, such as gathering manual data across multiple systems. A lot of effort is wasted on checking what turn out to be false positives, which account for 99% of screening results.
• Poor work quality: Inconsistency in decision-making across analysts and variability or human error in recorded audit trails creates a challenge. In the best case, this results in unnecessary work for L2 teams; but in a worst-case scenario, the bank can miss a true hit and get exposed to sanctions risk.
• Employee dissatisfaction: Tedious manual work creates a poor work experience for overworked employees, resulting in high turnover.
• Poor customer experience: Delays in account opening or payment processing and navigating an excess of touchpoints in the process all impact the customer experience.
• Compliance risks: Lack of adequate staffing, complexities of regulations plus increasing volumes, and human error all lead to compliance challenges faced by AML teams.

To remediate these inefficiencies, financial institutions should incorporate AI Digital Workers as part of the AML compliance teams.