Ripjar study shows GenAI set to reshape AML compliance

Ripjar has released new research indicating a significant trend towards the adoption of Generative AI (GenAI) solutions within compliance teams for customer Anti-Money Laundering (AML) screening and monitoring.

The research reveals that over 60% of compliance teams expect GenAI adoption to become widespread within their organisations over the next two years, marking a notable shift to integrating advanced technologies in addressing challenges related to financial crime prevention.

Through the use of GenAI, 98% of compliance leaders anticipate a substantial reduction in false positives, highlighting the potential for GenAI to improve both accuracy and efficiency in compliance processes.

The research identifies case management and decision support (at 37%) as the most prominent applications of GenAI in customer screening and monitoring, closely followed by contextual analysis of names at 35%. Additionally, 35% of compliance teams either currently employ or intend to employ GenAI to support investigations, showcasing its versatility in tackling complex compliance issues.

The findings suggest several key benefits from incorporating GenAI into compliance efforts, particularly for sanction screening, adverse media, and PEP checks. Half of compliance teams (50%) noted increased efficiency as a primary benefit, with improved accuracy at 48% and enhanced risk discovery at 47% following closely. Furthermore, 44% highlighted cost reduction, while 50% cited significant time-saving advantages, demonstrating that GenAI's benefits extend beyond mere speed and ease of work.

Gabriel Hopkins, Chief Product Officer at Ripjar, stated: "While there are structural issues to navigate, it is fantastic to see such broad consensus that GenAI offers a transformative opportunity for many areas in compliance, particularly in the fight against money laundering and terrorist financing. With its ability to enhance efficiency, improve accuracy and reduce false positives, GenAI is poised to change how compliance teams approach critical tasks such as extended customer due diligence. With the right support, these new technologies promise a future for compliance that is both more efficient – consuming fewer resources – and more effective – uncovering more true risk."

Addressing the potential challenges, Hopkins added, "We will encounter challenges as banks and financial organisations grapple with the best approach to adoption, particularly due to concerns about predictability and explainability in regulated environments. Further clarity from regulators will help to support organisations on the journey. Savvy organisations are planning now for how they can leverage the latest techniques to transform their approaches to compliance. The best solutions will combine different AI and machine learning techniques with the finely honed capabilities of traditional human-led compliance teams, ensuring a balanced and responsible approach."

The research involved 300 compliance leaders who are decision makers regarding AI within financial services organisations, surveyed across North America, Europe, the Middle East, and Asia.