Money laundering facilitates crime, threatens our national security, distorts markets, and has a devastating economic and social impact on citizens, according to the U.S. Department of the Treasury. Financial institutions are required to do their part to combat these threats.
The Financial Crimes Enforcement Network (FinCEN), a bureau of the United States Department of the Treasury, requires financial institutions to accurately and promptly report suspicious activities under the Bank Secrecy Act. When institutions fail to meet anti-money laundering (AML) compliance requirements such as maintaining adequate transaction monitoring systems, performing customer due diligence, and properly filing suspicious activity reports (SARs) they can be fined billions of dollars. This is on top of the more than $275 billion the industry already spends on AML compliance.
Lawmakers and policymakers know that AML compliance places significant cost and operational burdens on financial institutions. Through the AML Act of 2020 and Innovation Initiative—and the latest proposed rule—FinCEN is actively advocating for financial institutions to modernize and innovate their AML/CFT programs and combat financial crime, including an increased focus on risk-based processes and technology. This will hopefully reduce some of the compliance costs and improve effectiveness. There is a growing emphasis on integrating technologies to support and enhance human intervention and judgment, ultimately boosting operational efficiencies and reducing errors.
As we move further into 2025, incorporating AI into AML compliance programs will be crucial. Fortunately, many leaders in the financial industry are already turning to AI to keep up with the changing landscape. According to a recent report, 78 percent of financial institutions arelooking to technology to help automate processes and improve efficiency.
Here are four ways AI can help you update your AML program:
1. Transform Transaction Monitoring
Traditional transaction monitoring (TM) systems struggle to keep up with the complexities of modern financial crime. They rely on static rules that can't adapt to new criminal tactics, resulting in a flood of non-suspicious items or false positives that overwhelm compliance teams and obscure the real threats.
While many TM analysts were attracted to their job’s analytical work, many have found their days to be tedious—filled with performing data gathering tasks rather than leveraging their true talent for fighting financial crime, managing risk and mitigating it. In traditional compliance operations, upwards of 80-85 percent of analyst work is spent tracking down information and supporting evidence for case reviews. By automating this tedious, error-prone work and auto-populating the SAR narrative, AI drastically reduces mistakes, ensures complete information, and frees up the analysts to work on higher-value/higher-risk type of work — making them more strategic contributors to the program.
2. Automate Manual Compliance Processes
Many AML programs still rely heavily on manual processes, which are labor-intensive and error-prone, leading to compliance breaches and hefty fines. AI and automation technologies can handle these repetitive and time-consuming tasks, such as customer onboarding, sanctions screening alert review, and the filing of suspicious activity reports, to improve efficiency and accuracy while freeing up human analysts for higher-value work.
For instance, AI can automate the review and disposition of sanctions alerts, of which 99 percent are false positives. Automation can also streamline the SAR filing process by automatically generating SARs based on predefined criteria, reducing the risk of human error and helping banks maintain regulatory compliance.
3. Mitigate Staffing Challenges
The financial industry continues to grapple with a significant talent shortage in AML and sanctions compliance. Many banks have open positions that remain unfilled for months, and even when new analysts are hired, onboarding and training can take considerable time. High attrition rates further exacerbate these challenges, as trained analysts often leave for better-paying opportunities, creating a perpetual cycle of recruitment and training.
Banks can leverage AI to augment their existing teams. AI can handle routine tasks, such as screening alert disposition and data extraction, allowing human analysts to focus on complex investigations that require judgment and expertise.
AI-driven augmentation enhances productivity and helps banks scale their operations without constantly hiring and training new staff, particularly during periods of increased alert volumes. AI can step in to manage the surge, ensuring compliance standards are maintained without overburdening the team.
4. Enhance Regulatory Reporting and Compliance Accuracy
With regulatory scrutiny intensifying, accurate and timely reporting is more critical than ever. AI and ML improve the accuracy and efficiency of regulatory reporting by analyzing large datasets and identifying relevant information to ensure that SARs and other compliance reports are thorough and error-free.
Additionally, AI can provide deeper insights into a bank’s risk exposure by identifying complex networks of transactions that might indicate money laundering, enabling banks to take proactive measures to mitigate risks.
The Future of AML
There’s no doubt that criminals are getting smarter, alerts are multiplying, and regulatory scrutiny penalties are mounting. By incorporating AI into their AML programs, banks can stay resilient and effective in the fight against financial crime.
The future of AML lies in using AI to enhance human capabilities, making compliance programs more adaptable to the ever-changing landscape of financial crime. Embracing AI isn’t just about staying compliant, it’s about staying ahead and protecting our financial system from criminal activities. 
David Caruso is Vice President of Financial Crime Compliance at WorkFusion. With over 25 years in financial crime compliance, David has led major AML and sanctions programs at banks, including JP Morgan, Wachovia, Key Bank, and Riggs Bank.