The risk-based approach (RBA) has been a cornerstone of anti-money laundering (AML) compliance. Yet, the RBA has largely been deployed as a static governance framework rather than a responsive, operational tool. In today’s environment, that gap is becoming untenable.
A new webinar from SymphonyAI, explores how firms can leverage agentic AI as the engine of continuous risk assessment and control calibration.
Financial crime is no longer a periodic threat, it said. It is continuous, borderless, and accelerating. Criminal actors are innovating at a pace that consistently outstrips the compliance control cycles that firms rely upon to manage exposure. The bottleneck is not a shortage of data. The real challenge lies in translating that data into actionable risk intelligence fast enough to keep controls meaningfully aligned with the evolving threat landscape.
Regulators have recognised this reality. The supervisory conversation has moved on from whether a firm possesses an RBA framework to whether it can actively and continuously demonstrate that its controls reflect current risk.
Agentic AI represents a significant shift in what is now possible, SymphonyAI said. By continuously scanning and interpreting diverse sources of risk intelligence, agentic AI can power a new generation of compliance capabilities, including automated risk alignment, dynamic control calibration, typology identification and classification, customer risk profiling, and the prioritisation of compliance operations.
SymphonyAI AML, FinCrime and Sanctions subject matter expert Elizabeth Callan explored these capabilities in a dedicated session, focusing on three key applications of agentic AI in AML compliance. These included the automation of risk intelligence monitoring, the acceleration and enhancement of risk assessments, and the dynamic alignment of risk intelligence to control calibration.
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