The mandate from the U.S. Treasury’s FinCEN is clear—financial institutions must diligently report suspicious activities to comply with the Bank Secrecy Act, especially concerning AML protocols.
According to Workfusion, these include conducting thorough customer due diligence, monitoring transactions for illicit activities, and submitting timely suspicious activity reports (SARs). The consequences of non-compliance can be severe, exemplified by the staggering $3bn in fines imposed on TD Bank for AML oversights. The financial industry currently invests approximately $275m annually in AML compliance efforts.
Acknowledging the burdensome costs of AML compliance, FinCEN advocates for the modernization of AML/CFT frameworks to effectively counteract financial crimes without the exorbitant expense. The goal is not to replace human workers but to enhance their efficiency and reduce errors through technology, specifically artificial intelligence (AI).
AI’s emergence as a critical tool in AML compliance is increasingly recognized across the financial sector. A significant 78% of financial institutions now turn to technology to streamline operations and boost efficiency. Those lagging in AI adoption may soon find themselves at a competitive disadvantage or even facing regulatory challenges.
A case in point is Valley Bank’s recent adoption of WorkFusion’s AI Agent for automating sanctions alert adjudication. This AI implementation has brought substantial benefits, as noted in a February 2025 case study:
· Automated review of over 20,000 alerts monthly
· Achieved a 65% automation rate in sanctions alert reviews
· Significant time savings for employees, leading to faster payment processing and improved customer experience
These enhancements are critical as they demonstrate AI’s capacity to revamp transaction monitoring systems plagued by outdated static rules that often generate excessive false positives. By automating the grunt work and refining SAR narratives, AI substantially decreases errors, ensures complete data capture, and allows analysts to focus on more strategic, high-value tasks—thereby increasing job satisfaction and reducing turnover within compliance departments.
Furthermore, AI’s role extends to automating traditional, manual AML tasks like customer onboarding and sanctions screening, often riddled with human errors and inefficiencies. For example, AI systems can automatically resolve 99% of false positive sanctions alerts and streamline SAR filings, enhancing overall regulatory compliance and reducing the likelihood of costly breaches.
The persistent talent shortages in the financial sector exacerbate these challenges, with banks struggling to fill roles with qualified personnel. AI can mitigate these issues by
taking over routine data processing tasks, thus allowing human analysts to dedicate their expertise to complex cases that require nuanced judgment.
Lastly, as regulatory demands grow, the accuracy and timeliness of reports become crucial. AI and machine learning enhance the precision and efficiency of these reports, providing deeper insights into transaction networks potentially indicative of money laundering activities, thereby enabling proactive risk management.
Copyright © 2025 RegTech Analyst
Copyright © 2018 RegTech Analyst
