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In the recent Celent Dimensions Survey 2025 of 232 Risk & Compliance executives, the top-ranked priority driving IT investment decisions is currently “AI for Efficiency.” While some may find prioritizing AI in compliance operations surprising, the fact is AI has been used by financial crime compliance (FCC) programs at banks worldwide since 2016.
FCC leaders didn’t boil the ocean when they started with AI. Instead, they started small by doing such things as applying machine learning and natural language processing to document reviews. Their main goal was the same as today – to gain efficiency by automating highly manual and repetitive tasks.
Today, regulators and compliance leaders at financial institutions are promoting and planning widespread adoption of AI-based technologies to both gain more efficiency and de-risk operations. The U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC) and the UK’s Financial Conduct Authority (FCA) expect financial services firms to adopt AI. For example, the FCA’s April 2024 AI Update report states, “Our own use of AI is not just changing markets but also the way we regulate. Advanced models can help identify fraud and bad actors, for example.”
Despite the significant uptake of AI by FCC programs and the encouragement to do so by regulators, some compliance leaders remain hesitant to adopt AI. They provide three main reasons behind their hesitancy. In this post, we will alleviate the fears that are making them hesitant to reap the massive benefits of AI in FCC operations.
Fear #1: Our systems are not modernized to handle AI
Many banks and other FIs have implemented a wide range of technology over the years, much of it not designed with AI in mind. While many of their systems do not incorporate AI or account for it, they can still remain in place while taking advantage of AI.
Typically, Application Programming Interfaces (APIs) make it simple to integrate AI with older systems, and AI vendors often pre-build connectors or provide an API framework for creating the necessary connections. As a result, data can move between existing systems and AI.
Fear #2: Our data must be fixed and secured before we adopt AI
It is true that AI thrives off data and bad data can lead to AI making wrong decisions. Yet, specific to financial crime compliance, companies like WorkFusion have been working with massive volumes of data – especially data related to anti-money laundering (AML) and other financial crime prevention areas. This means our AI solutions for FCC come with pre-trained models for multiple use cases and make an immediate impact. Then, over time, they leverage the machine learning aspect of AI to learn more about a specific institution’s data.
For example, the WorkFusion AI Agent named Evan performs Adverse Media Monitoring alerts review. Evan arrives with pre-built industry knowledge, and his machine learning ensemble can analyze new news content from the start.
As for data security, due to the popularity of LLMs like ChatGPT, many people assume that LLMs are always accessed via the cloud and that data used by AI must move outside of the organization’s firewall, becoming insecure. However, cloud-accessed LLMs are not the only AI option. LLMs like Llama (by Meta) and Mistral AI can be installed on-premises and run within an organization’s firewall. Multiple customers of WorkFusion run Evan and two of our other AI Agents (named Evelyn and Tara) inside their firewall to perform sanctions screening alert reviews.
When AI data remains secure, FCC teams gain the benefits of AI without the risk of exposing critical data.
Fear #3: We don’t have enough staff who are AI-savvy
Without a doubt, early adopters of AI in 2016 had to have technical staff to deal with budding AI solutions. That is not the case today. Just like with thousands of other technologies, AI companies have made their offerings intuitive and easy to use when the vendors have years of experience serving a specific target market. Such is the case with WorkFusion’s AI Agents for Financial crime compliance.
Our AI Agents are smart, arrive pre-trained, and collaborate with your team whenever a decision cannot be made automatically. So, you don’t need to become a data scientist or machine learning engineer. You can even be a technology laggard. This is why large banks and smaller FIs can both leverage AI today. It used to be that smaller banks lacked the staff to work with AI. But with AI becoming simplified via AI Agents, WorkFusion counts among our customer base a blend of both large and small institutions. In fact, Valley Bank has automated their sanctions alert adjudication process using an AI Agent for faster payments and better employee experience. They achieve a 65% automation of reviews of sanctions hits on over 20,000 alerts per month. Their FCC leadership essentially hired our AI Agent like they would an employee and never needed to become AI developers. You can read about Valley Bank’s adoption of our AI Agent here.
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