By Adan Perez, VP of Product and Innovation, RadarFirst
Artificial intelligence is transforming financial services faster than any technology we’ve seen in decades. AI now drives payment orchestration, fraud detection, credit intelligence, and embedded finance at a scale that would have been impossible just a few years ago.
That speed creates an undeniable opportunity. It also creates a new category of risk that many financial institutions are not prepared to manage. From my perspective, one thing is clear. Privacy and compliance teams must play a central role in AI governance if organizations want to scale AI responsibly in 2026 and beyond.
AI in Payments, Fraud, and Lending Changes How Risk Emerges
AI allows financial institutions to make decisions faster and across far larger populations. Fraud models analyze thousands of signals in real time. Credit decisions are automated end-to-end. Payment routing systems dynamically optimize transactions without human intervention.
The challenge is that AI rarely fails in obvious ways.
“AI typically works exactly as designed,” says Zach Burnett, Chief Executive Officer at RadarFirst. “The risk comes from how quickly it scales decisions and how hard it becomes to see where governance hasn’t kept up.”
In financial services, those blind spots matter. When AI-driven decisions affect access to credit, payment approvals, or fraud outcomes, even small governance gaps can create significant regulatory and reputational exposure.
Why AI Governance Now Belongs With Privacy and Compliance Teams
AI governance has traditionally been viewed as a technical responsibility. That approach no longer works in highly regulated industries like finance.
AI systems rely on personal data, automate regulated decisions, and increasingly incorporate third-party and embedded models. When issues arise, they surface as privacy questions, compliance investigations, or customer complaints, not engineering defects.
As someone who works closely with privacy and compliance teams, I see the same challenge repeatedly. They are accountable for AI outcomes but lack visibility into how AI systems are being used across the organization.
AI governance provides that visibility. It creates clarity around purpose, data usage, accountability, and acceptable risk. Without it, privacy and compliance teams are asked to manage exposure reactively, after harm has already occurred.
AI Incidents Are Not Traditional Privacy Incidents
One of the biggest misconceptions in finance is that AI risk will look like a data breach. In reality, AI incidents are more subtle and often more damaging.
They show up as unexplained automated decisions, models used beyond their original scope, or third-party AI updates that change outcomes without notice. These scenarios may not trigger security alerts, but they still represent privacy and compliance failures.
“An organization doesn’t need to lose data to have a serious AI incident,” says Christopher Washington, Chief Technology Officer at RadarFirst. “If you can’t explain how an AI-driven decision was made or justify the data behind it, you’re already in trouble.”
Most privacy incident response programs were not designed for this reality. They focus on access and exposure, not automated decisioning or model behavior.
Why Privacy Incident Response Must Evolve for AI
As AI becomes embedded across financial workflows, privacy incident response must evolve alongside it.
Organizations need processes that can address:
- AI purpose creep and unapproved reuse
- Bias or discrimination identified after deployment
- Vendor and embedded AI changes that introduce new risk
- Inability to explain or justify automated outcomes
From my perspective, AI governance and incident response cannot be treated as separate efforts. Governance identifies risk early. Incident response provides a structured way to act when something goes wrong.
Without both, financial institutions are left reacting to AI issues without context, documentation, or clear ownership.
AI Governance Enables Innovation, Not Hesitation
AI governance is often misunderstood as a brake on innovation. In practice, it is what allows financial organizations to move faster with confidence.
When privacy and compliance teams are involved early, AI initiatives can expand without creating hidden exposure. Organizations gain the ability to scale AI-powered payments, fraud prevention, and embedded finance while maintaining trust and regulatory alignment.
“Governance isn’t about slowing teams down,” Burnett says. “It’s about making sure opportunity and accountability scale together.”
As we look toward 2026, AI will continue to redefine financial services. The organizations that succeed will be the ones that recognize governance as a strategic capability, not an afterthought. Privacy and compliance teams are no longer supporting players in this transformation. They are essential to making AI work safely, responsibly, and at scale.
About Author:
Adan Perez is the VP of Product and Innovation at RadarFirst, where he focuses on building privacy incident management and AI governance solutions that help organizations operationalize compliance in highly regulated industries.
