On October 20, Nasdaq Verafin hosted the AFC Leaders Summit at Nasdaq MarketSite in New York City to discuss the next era of financial crime prevention. The event brought together leaders from financial institutions and law enforcement to technology providers, intergovernmental associations and industry advocates — the people closest to the fight against financial crime. And for a single reason: Crime is evolving, and no organization can solve it alone.
Discussions in the room centered around a shared understanding of the challenges faced by the industry and conviction for action.
The stakes are clear. Our 2026 Global Financial Crime Report estimates that $4.4 trillion in illicit funds moved through the global economy in 2025, an increase of more than $1 trillion in just two years. Fraud losses alone surged to $579.4 billion. And 90% of the anti-financial crime professionals surveyed in our report noted an increase in AI-driven attacks at their institution.
This is the reality we are operating in today.
The question is no longer whether the industry needs to adapt. It is how we will adapt quickly enough to get ahead. The next era of financial crime prevention will demand bold decisions. After conversations at the Summit, five clear challenges emerged for our industry.
1. Innovate with Urgency, Purpose and Conviction
Artificial intelligence is transforming all aspects of financial crime. Bad actors are using AI to automate attacks, cross borders and personalize deception at unprecedented speed. Evolutions in technology have erased constraints that once limited the scale and sophistication of criminal tactics.
But this same technology is also creating entirely new possibilities for those of us on the side of justice. AI allows financial crime fighters to move from institution-level visibility to network-level intelligence. From reactive investigations to proactive intervention. And from resource-intensive manual operations to AI-powered workflows that focus human expertise on higher-impact analysis and decision-making.
During our Summit’s innovation panel, discussion centered around what it takes to move AI from pilot to production. The conversation was grounded in practice, not theory. Real workflows, real results and real lessons from institutions that have been through it.
Financial institutions have always been leaders in digital transformation. Now we must challenge ourselves to bring that same innovation-first mindset to financial crime prevention.
This is not about elevating financial crime technology for its own sake. Our innovation must be purposeful, measured and grounded in proven outcomes, built to deliver value at scale. Broader adoption of anti-financial crime innovation creates a stronger network, raising our collective ability to detect and disrupt financial crime.
2. Make Governance the Foundation, Not the Friction
Responsible AI governance and security are the foundation that allows institutions to innovate with confidence.
At the AFC Leaders Summit, a panel explored governance models, controls and accountability structures needed for responsible AI adoption. Agentic AI requires control beyond traditional model risk management. Autonomous decision-making, human-in-the-loop oversight and end-to-end auditability are baseline requirements for responsible deployment.
Strong governance should establish guardrails that allow teams to push boundaries and enable innovation, not gatekeep and slow it down. Compliance allows institutions to adopt AI while maintaining the trust, control and oversight this technology demands.
Institutions that get this right will be positioned to act with urgency, because governance will enable sustained progress.
3. Prove in Production and Scale What Works
The results of scaling financial crime prevention with agentic AI speak for themselves. Over 850 financial institutions are using our Agentic AI Workforce. They are seeing up to a 90% reduction in sanctions alert workload and a 50% reduction in enhanced due diligence review time. These are real outcomes, delivered in real environments.
Reflecting on FNBO’s experience with our Agentic AI Workforce Nick Baxter, Chief Risk Officer said: “The time for financial institutions to implement agentic AI is now. The efficiency it offers may soon be essential, if not expected, for AML/ CFT compliance — and the growing sophistication of bad actors leaves little alternative.”
Agentic AI is proven in production. The challenge now is scaling that impact — extending established results across more institutions, use cases and the AFC workflow.
As financial crime evolves, the approaches used to prevent it must evolve just as quickly. The capabilities available to fight financial crime have fundamentally changed, and the evidence of their impact is already here. The question is whether the industry will scale what works with the urgency required to stay ahead of evolving threats.
4. Respond to a Network Threat with a Network Defense
Criminal networks operate across institutions, sectors, borders and channels and they adapt faster than any single organization can respond. The gap between their reach and ours is where illicit activity thrives.
Our fraud and scams panel at the Summit brought together leaders in the FBI, the UN Office on Drugs and Crime, the American Bankers Association and Nasdaq Verafin. Their conclusion was shared: No single institution or agency can disrupt these networks alone.
The value of financial crime intelligence grows when institutions contribute to and draw from a shared network. Connecting intelligence across institutions closes critical gaps in visibility, revealing patterns and relationships that would otherwise remain hidden, allowing us to put that insight into action.
As Adena Friedman, Chair and CEO of Nasdaq, said during her session at the Summit: “The institutions and economies that succeed will be those that are willing and able to combine insights, share intelligence responsibly and coordinate actions to protect the financial system more effectively than any one organization could alone.”
Financial crime investigation can no longer be siloed. The challenge is moving from talking about collaboration to operationalizing by turning shared intelligence into collective action.
5. Remember the Human Cost
Financial crime is not abstract. Behind every fraudulent transaction, laundered dollar and successful scam, a real person bears the consequence.
When we talk about financial crime prevention in terms of alert volumes and detection rates, we risk losing sight of what we are fighting for. The numbers are real. So are the consequences.
The Path Forward
The pace of financial crime demands a new level of urgency and collaboration. We need to innovate with conviction, govern with purpose, scale what is proven, respond as a network and never lose sight of the people behind the numbers.
At Nasdaq Verafin, our vision is to create the world’s most effective crime-fighting network, where trusted data, advanced AI and collective intelligence work together to help institutions stay ahead of evolving threats.
The future of financial crime prevention will not be defined by the sophistication of criminal networks. It will be defined by how effectively we work together to outpace them.
This work continues, and so does the conversation. I look forward to bringing our industry together again at Nasdaq MarketSite for the next AFC Leaders Summit on October 5, 2027.
FAQs
What is financial crime prevention?
Financial crime prevention encompasses the systems, processes and technologies financial institutions use to identify suspicious activity, investigate potential financial crime and prevent illicit funds from moving through the financial system. This includes fraud, money laundering, sanctions evasion and terrorist financing.
What are the benefits of AI in preventing financial crime?
AI helps institutions detect financial crime more efficiently and with greater accuracy, allowing teams to increase investigative capacity without increasing team size. Financial institutions are using agentic AI to handle routine manual tasks, allowing investigators to focus their expertise on higher-impact analysis and decision-making. In production today, institutions are seeing up to a 90% reduction in alert workload and a 50% reduction in case review time.
How is AI used in fraud detection?
Using AI for fraud detection encompasses machine learning models and agentic AI systems that analyze patterns, flag anomalies and autonomously triage alerts. Unlike traditional rules-based systems that generate high volumes of false positives, agentic AI-powered fraud detection reduces alert noise, connects signals across transaction monitoring and KYC processes, and accelerates the path from detection to resolution. All while maintaining transparency and auditability to support regulatory compliance.
How do AI agents support transaction monitoring and AML compliance?
AI agents autonomously triage alerts, make determinations and document decisions with full audit trails, maintaining human-in-the-loop oversight while scaling to handle greater volumes than manual processes. For AML compliance, this means consistent, audit-ready documentation across every alert, faster review cycles and the ability to focus analyst attention on the cases that require human judgment.
About the Author
Scott Peddle
VP, AML Product Strategy
As Vice President, AML Product Strategy, Scott leads product strategy for the Agentic AI Workforce at Nasdaq Verafin. A Software Engineer by background, Scott has spent years across Nasdaq Verafin’s Software Development, Product Management and Customer Success teams, giving him a deep understanding of the full customer experience. He is a passionate public speaker, problem solver and customer evangelist. His dedication to Nasdaq Verafin’s customers is fueled by a belief that successful customer partnerships and a commitment to working together are key in staying ahead of fraudsters in the world of financial crime.
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