Second-generation AI agents are transforming the landscape of financial crime compliance, bringing new levels of collaboration between people and machines.
The rapid pace of artificial intelligence adoption within the financial services sector has led to the emergence of these advanced agents that work together—and with human analysts—to detect, investigate, and mitigate financial crime more efficiently than ever before, claims Workfusion.
In a recent episode of the What the FinTech Podcast, Paul Hindle, editor of FinTech Futures, sat down with David Caruso, vice president of financial crime compliance at WorkFusion, to explore how AI agents are evolving within anti-money laundering (AML) operations. The discussion shed light on the growing trust financial institutions have in AI technology and its role in reshaping compliance strategies.
According to Caruso, banks’ acceptance of AI has shifted dramatically in the past two years. “We know that because we’re in the business of developing AI agents and we have a lot of clients, we have a lot of customers, and we have no trouble having conversations with banks of all sizes all over the world who have shown real interest in it,” he said. WorkFusion now counts ten of the world’s top twenty banks among its customers—a strong indication that AI is becoming integral to financial crime compliance operations.
This increasing acceptance began with the first generation of AI agents, which automated essential AML tasks such as data gathering, organisation, and information presentation. These tools significantly boosted efficiency across compliance teams, helping analysts spend less time on repetitive work and more on high-value investigations.
Now, the sector is witnessing the rise of second-generation AI agents. These agents go beyond simple automation, using investigative reasoning to assess whether unusual activity reflects legitimate behaviour or potential criminal intent. “Are there indications that these transactions are out of the norm for this customer? Is that indicative of just a change in their business or lifestyle, or is that indicative of perhaps financial crime, something nefarious that we need to look at?” Caruso explained.
WorkFusion’s second-generation agents exemplify this leap forward. They can filter thousands of potential red flags into just a few meaningful findings, reducing manual workload for analysts by up to 80%. Each of WorkFusion’s six AI agents plays a distinct role across the FinCrime compliance process: Tara handles transaction screening alerts, Evelyn manages sanctions and PEP reviews, Evan oversees adverse media screening, Isaac monitors AML transactions, Kayla performs customer ID verification, and Edward conducts enhanced due diligence.
These agents don’t work in isolation—they collaborate with each other and with human analysts. For instance, Edward uses intelligent document processing (IDP) to extract and structure complex data, applies machine learning (ML) to detect anomalies and risks, and leverages natural language processing (NLP) to interpret unstructured media reports or filings. Generative AI (GenAI) then helps him produce written summaries and visualisations for analysts, turning raw data into actionable insights.
Such agent-to-agent and agent-to-human collaboration defines this new generation of compliance technology. By filtering vast datasets and surfacing only the most suspicious items, these systems empower people to focus on high-value investigative tasks—strengthening both operational efficiency and the integrity of the financial system.
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