There is no question that financial service providers frequently struggle to find the time and resources to recruit and train staff , manage volume surges and comply with emergent regulatory demands .
These anti-money laundering ( AML ) compliance disruptions require that teams be resilient and adaptable . How do teams smoothly navigate these disturbances and overcome them ?
The answer is to adopt a technology-enabled risk-based approach that ensures organizations are using resources efficiently . According to the Financial Action Task Force ( FATF ), “ a risk-based approach should be the cornerstone of an effective AML / CFT [ combating the financing of terrorism ] program and is essential to properly managing risks .” 1 One of the most effective ways to quickly enable a risk-based approach is to augment existing technologies with advanced automation .
Leveraging advanced automation
Organizations are adopting solutions designed to automate a variety of AML compliance operations using artificial intelligence ( AI ). The key objective is to lower risk while making the work of compliance teams more efficient , so they spend less time performing manual tasks and can be more agile when program disturbances occur .
Automated data gathering and risk analysis speed up what is typically a time-consuming process by analyzing the correct data and presenting that analysis in a way that empowers investigators to make quicker decisions . Leveraging AI-enabled solutions speeds up processes , such as alert reviews , case investigations , excluding false positives , resolving backlogs , tackling remediation and keeping up with high-risk customer reviews .
Automation accomplishes these tasks efficiently by reducing a team ’ s research workload and allowing investigators to spend time with the cases that most demand their attention . For example , a solution that scores risk so that less experienced analysts can manage basic cases and more seasoned investigators can tackle complex alerts .
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