SEGMENTATION AND AI IN AML ALERTS
R&C: Could you provide an overview of how technology is transforming financial institution’s (FI’s) anti-money laundering (AML) processes?
Angotti: Technology enhancements in financial institutions (FIs) are becoming indispensable to managing financial crime risk. Regulators expect FIs to make use of the enormous amount of data they have about their customers and their customers’ transactions. The only way to effectively identify risk from all of this data is through technology. The United Nations Office on Drugs and Crime estimates that money laundered globally is about 2-5 percent of world GDP annually, about $3 trillion. In addition, the number of noncash transactions will increase as mobile technology – mobile wallets and mobile money transfers – are introduced into the global market and emerging markets. For the past few years, FIs have wrestled with methods to minimise loss, remain efficient and maintain proper regulatory compliance. Technology is transforming FIs’ anti-money laundering (AML) processes by efficiently sorting through large amounts of data, developing more useful predictive modelling and using client segmentation and behavioural patterning. Technology has the potential to better identify risk, by eliminating some of the ‘noise’ in the data and by enabling compliance personnel to concentrate on actual risk.
LaScala: Over the past few years, FIs have begun to embrace robotic process automation to expedite their more tedious work. This is achieved by either business process automation or by using ‘bots’ designed to perform automated and repetitive tasks. As such, AML analysts and investigators derive increased efficiencies and get to focus on the AML typologies, rather than gathering and exhibiting investigative artefacts. This shift in focus results in increased quality, productivity and employee satisfaction. At the same time, tremendous strides in artificial intelligence (AI) and machine learning (ML) are working to increase the quality of AML alerts while decreasing the volume. Access to this broader collection of cognitive tools, which have evolved significantly in recent years to include ML, deep learning and advanced cognitive analytics, will, no doubt, yield remarkable benefits relating to the effectiveness and efficiency of AML transaction-monitoring systems.
Apr-Jun 2019 Issue