I’ve spent most of my career working in and alongside financial institutions as they’ve adapted to changes in the landscape. New regulations, technologies, financial crime typologies and expectations have continually reshaped how our industry operates.
Often these shifts are gradual. But rarely, there are moments so consequential that the changes are brisk and pivotal.
From my time at ACAMS Las Vegas this year, I can tell we are in the latter.
Across conversations with compliance leaders, fraud professionals, investigators and executives, there was a shared recognition that the industry is confronting a new reality. Financial crime is growing more organized, and more interconnected. At the same time, financial institutions are gaining access to new technologies and intelligence that have the potential to fundamentally change how risk is identified and investigated.
What struck me most is how firmly AI has become the foundation of these conversations. The question many leaders are asking is no longer whether AI will play a role in anti-financial crime. AI is here, and institutions are embracing it. The focus is increasingly on what comes next: how institutions can build on AI to address the bigger challenges facing the industry.
I’m hearing many of the same themes across Nasdaq Verafin’s Agentic AI Workforce Tour. Those conversations are becoming increasingly practical, focused on how agentic AI can help expand investigative capacity today, while also exploring how broader intelligence can help uncover risks that individual institutions cannot see on their own.
The Industry Is Facing a New Challenge
Financial crime doesn’t respect organizational boundaries. It hasn’t for a long time.
Criminal organizations operate across institutions, geographies, payment rails, products and counterparties. Fraud rings, mule networks, money laundering operations and other forms of organized financial crime rarely exist within the boundaries of a single institution.
Yet many anti-financial crime programs still rely primarily on institution-level visibility. Investigators often see only the activity occurring within their own organization, while the broader network remains largely hidden. The result is a growing gap between how criminals operate and how the industry is able to respond.
As I spoke with leaders at ACAMS, it became clear that many recognize this challenge. The industry understands that increasingly networked threats require a more connected approach to detection and investigation.
Why Agentic AI Is Gaining Momentum
One reason agentic AI has generated so much attention is because it addresses a problem every institution lives with: capacity.
Alert volumes continue to grow. Cases become more complex. Investigators are expected to do more, with less, while maintaining the quality, consistency and auditability that regulators and institutions require.
Agentic AI offers an opportunity to reduce manual effort, streamline investigations and let investigators spend more time applying their expertise where it matters most.
That was a recurring topic at ACAMS and throughout conversations we’re having across the industry. Institutions are increasingly interested in practical applications of AI that can help teams become more effective and efficient while preserving transparency and oversight.
But what I found particularly interesting is how quickly many conversations progressed beyond efficiency gains.
Once leaders begin to understand how AI can help address capacity constraints, the next question naturally follows: what should we do with that additional capacity?
Looking Beyond Institution-Level Risk
This is where the conversation gets interesting, and where AI becomes even more central to the discussion.
Financial crime is increasingly a network problem. Criminals do not organize themselves around the structure of financial institutions. They operate across institutions and channels, creating relationships and patterns that can be difficult to identify when viewed through the lens of a single organization.
As a result, we’re seeing growing interest in consortium intelligence, network-driven detection and broader views of risk.
The opportunity is not simply to investigate alerts faster. The opportunity is to better understand the relationships, behaviors and connections that may indicate coordinated criminal activity.
This is one of the themes that came up repeatedly at ACAMS: combining network-driven detection with agentic investigation to help uncover connections and risk patterns that extend across institutions and reveal criminal networks that might otherwise remain hidden.
When combined with agentic AI, that intelligence becomes significantly more actionable. AI can help institutions operationalize large volumes of information, surface meaningful relationships and support investigations in ways that would be difficult to achieve through manual processes alone.
In other words, AI is helping institutions work faster and see more. That broader visibility is what makes the technology so powerful.
A Conversation Worth Watching
I’ve seen our industry evolve many times throughout my career.
The institutions that adapt most successfully are rarely those that focus solely on the newest technology. They’re the ones that recognize when the nature of the problem itself is changing.
That’s why the conversations coming out of ACAMS Las Vegas resonated with me.
Yes, agentic AI is helping institutions address one of the industry’s most immediate challenges: capacity.
But the bigger conversation is about visibility, and AI is at the center of that too.
As financial crime becomes increasingly connected, institutions are exploring how agentic AI, consortium intelligence and network-level detection can work together to provide a broader understanding of risk and a more effective approach to disrupting criminal activity.
To me, that’s the conversation worth paying attention to.
Because while AI may be accelerating change, the larger shift is how the industry thinks about financial crime itself. AI is the tool making that shift possible. And that combination may ultimately prove far more significant than the technology alone.
FAQs
How is AI being used in anti-financial crime (AFC) programs?
Financial institutions are increasingly adopting AI — particularly agentic AI — to empower their anti-financial crime programs. Agentic AI helps reduce manual effort, streamline investigations and allow investigators to focus their expertise where it matters most, while maintaining the quality, consistency and auditability that regulators and institutions require.
What is Agentic AI and why is it gaining attention in financial crime detection?
Agentic AI is a form of artificial intelligence that can autonomously perform investigative tasks to support anti-financial crime efforts. It has generated significant attention because it directly addresses a problem every financial institution faces: growing alert volumes, increasingly complex cases and the need to do more with less. By reducing manual effort, agentic AI expands investigative capacity and lets human investigators apply their expertise where it has the greatest impact.
Why is financial crime considered a network problem?
Financial crime is increasingly a network problem because criminal organizations operate across institutions, geographies, payment rails, products and counterparties. Fraud rings, mule networks and money laundering operations rarely exist within the boundaries of a single institution. This makes criminal networks difficult to identify when viewed through the lens of one organization, driving growing interest in consortium intelligence and network-driven detection that provide a broader view of risk.
How can financial institutions uncover criminal networks that extend beyond their own data?
Institutions are combining agentic AI with consortium intelligence and network-driven detection to uncover cross-institutional risk patterns. This connected approach allows institutions to see relationships and criminal activity that would remain hidden when relying solely on their own data. By bringing together network-level intelligence with agentic AI-enabled investigative capabilities, institutions can make that broader visibility significantly more actionable in disrupting organized financial crime.
About the Author
Chuck Taylor, JD, CAMS, CAFP
Head of AML Commercial Strategy
Bringing a wealth of knowledge and experience from previous executive roles in consultancy and banking, Chuck now serves as the Head of AML Commercial Strategy for Nasdaq Verafin and is tasked with aligning sales execution with product vision and go-to-market strategies, empowering sales teams and unlocking growth across diverse customer segments. Chuck works closely with product management teams, business leaders and clients to ensure that the company’s offerings are aligned with current regulatory standards and effectively address the evolving challenges in the financial services industry.
In addition, Chuck serves as a subject matter expert on AML regulations, industry trends and best practices for financial crime prevention for Nasdaq Verafin. His role involves providing strategic insights to enhance the company’s financial crime solutions and assisting clients in navigating complex compliance requirements.
