A recent survey conducted by SAS in partnership with KPMG has highlighted the slow pace of AI and machine learning (ML) adoption in anti-money laundering (AML) processes, with only a small portion of financial institutions having implemented these technologies fully.
The global survey engaged 850 members of the Association of Certified Anti-Money Laundering Specialists (ACAMS) and found that only 18% of participants have AI/ML solutions currently active. An additional 18% are engaged in piloting these technologies, while 25% plan to implement them within the next 12 to 18 months. However, a significant 40% of respondents have no current plans to embrace AI/ML technologies.
Interest in generative AI (GenAI) is evident, with nearly half of respondents either piloting GenAI (10%) or exploring its potential (35%). Despite this, 55% of those surveyed have no plans to integrate GenAI technology. The study highlights challenges such as regulatory reluctance and budgetary constraints as primary obstacles to full adoption.
Kieran Beer, Chief Analyst and Director of Editorial Content at ACAMS, commented on the findings: "The survey indicates that AML practitioners believe regulators have cooled on AI. Fifty-one percent said their regulator promotes or encourages AI/ML innovation—a 15-point drop from 2021. Those who said regulators are apprehensive or cautious about AI/ML adoption rose from 28% to 36%, and those describing regulators as 'resistant to change' more than doubled from 6% to 13%."
Additionally, the survey shows that the expectation for AI and ML in reducing false positives is becoming more pronounced. A growth has been seen with 38% of AML specialists identifying this as a key area for AI/ML deployment, up from 30% in 2021. Other areas where AI/ML is expected to contribute include automating data enrichment and detecting new risks, although interest in these aspects has dipped slightly.
Timo Purkott, Global Fraud and Financial Crime Transformation Lead at KPMG International, noted: "AI and machine learning aren't a magic fix for every financial crimes challenge. But they are showing to be increasingly effective in certain areas—especially those involving large amounts of data. That includes automating alerts from transaction monitoring, generating enterprise wide risk assessments, reporting suspicious activities, AML checks, striving to reduce false positives and more. It all depends on data. Organisations must invest in their data management infrastructure to maximise the value of AI and ML and stay ahead of financial criminals."
The survey offers wide-ranging insights into the current state of AI/ML adoption and the reasons for hesitation among financial institutions. A noticeable shift is observed where the perceived barrier of budget constraints has dropped from 39% in 2021 to 34%, being overtaken by the lack of a regulatory imperative, which has risen to 37%. Furthermore, the need for skills is decreasing as a concern, with only 11% citing it as a challenge compared to previous reports.
Stu Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions at SAS, highlighted the importance of data integration: "The key to unlocking the full potential of AI and machine learning is integration of data sources, teams and technology. The first step toward that integration is establishing a data ecosystem that combines data from all sources. In this ACAMS survey, 86% of respondents reported doing some form of integration between AML, fraud and information security processes. Nearly a third have a fully integrated case management capability across those functions. Another third collaborate through cross-functional teams to deploy controls to prevent financial crimes exposure. Some organisations may be waiting on regulatory guidance. Firms that press ahead with integrating data and operations with governance in mind are laying the groundwork for responsible innovation in AI and ML and will enjoy a competitive advantage over those who hesitate."
The survey elucidates advancements and the hurdles related to AI and ML in AML, illustrating the need for ongoing dialogue and investment in regulatory and data infrastructure to harness the full potential of these technologies.
