Four Ways AI Can Transform AML Compliance Operations

The U.S. Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) requires financial institutions to report suspicious activities under the Bank Secrecy Act – particularly as they relate to money laundering. Institutions must meet anti-money laundering (AML) compliance requirements such as performing customer due diligence, monitoring customer transactions for signs of illegal activity, and filing timely suspicious activity reports (SARs). Failure to perform these activities results in significant fines and penalties. Such is the recent case of the $3 billion in fines and penalties levied against TD Bank for its anti-money laundering failures. Overall, the industry spends $275 on AML compliance.

Because AML operations are labor intensive, lawmakers and policymakers recognize that AML compliance places significant cost on financial institutions. This is why FinCEN has taken an active role in advocating for financial institutions to modernize their AML/CFT programs to combat financial crime. If risk-based processes can be streamlined and made more effective with technology, compliance costs will come down dramatically. Hence, there is a significant focus on integrating technologies to support and enhance human performance. This does not mean eliminating human workers, but rather, boosting operational efficiencies and reducing errors.

By now, you can probably guess which technology is key to making technology-driven processes smarter and more efficient – AI.

Incorporating AI into AML compliance programs is fast-becoming table stakes for any financial institution. In fact, 78 percent of financial institutions are looking to technology to help automate processes and improve efficiency. Banks and other institutions that fall behind in applying AI to AML compliance operations will quickly fall behind their peers, and eventually, out of favor with regulators.

Consider the case of Valley Bank

Valley Bank recently implemented a WorkFusion AI Agent to automate sanctions alert adjudication for faster payments and better employee experience. According to the February 2025 case study, the use of AI has already delivered multiple, measurable returns on investment:

• • Automated alert review for over 20,000 alerts per month

 

• • 65% automation rate for sanctions alerts review

 

• • Freed up employee and resource time

 

• • Better customer experience via faster payments

 

The Valley Bank case is not unusual. It points to four of the simplest ways that AI can help any financial institution update their AML program. Here’s an overview of the four ways.

1. Transforming Transaction Monitoring

Since they rely on static rules that cannot adapt to new criminal tactics, traditional transaction monitoring (TM) systems struggle to keep up with the complexities of modern financial crime. To compensate, they send out alerts for too broad a swath of situations, and that results in a deluge of non-suspicious items (called false positives) that overwhelm compliance teams and obscure the real threats.

In traditional compliance operations, analysts spend up to 85 percent of their time 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. This makes them more strategic contributors to the program. It also provides them with greater job satisfaction, reducing the employee churn rates that plague compliance teams today.

2. Automating 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. Mitigating Staffing Challenges

The talent shortage that hit the financial industry several years ago continues unabated. So, banks fail to fill open positions and are forced to hire less-qualified staff who take months to fully onboard and train. And many of these new hires do not work out or leave for better-paying, more fulfilling opportunities. It leads to a costly perpetual cycle of recruitment and training.

AI can alleviate this problem by augmenting existing AML compliance 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 to scale operations without constantly hiring and training new staff, particularly during periods of increased alert volumes.

4. Enhancing Regulatory Reporting and Compliance Accuracy

As regulatory scrutiny intensifies, accurate and timely reporting becomes ever more critical. AI and machine learning (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 accurate and comprehensive.

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 is bright for AML

The U.S. Treasury’s FinCen recognizes that money launderers and other criminals are using advanced technologies to circumvent banking regulations and perform nefarious activities. So, regulators are sending a clear message to financial institutions to adopt AI and other technologies to keep pace with criminal networks. It’s the only way to stay resilient and effective in the fight against financial crime.

When your bank embraces AI, not only can you position yourself to avoid falling victim to crime, fines and penalties, your organization can become higher performing and operationally far more efficient.

Visit the WorkFusion Resources page or request a demo to learn more.