I handed my microphone over to Hailey Windham, host of Fraud Forward. For once, I'm the one answering the questions instead of asking them.

Hailey opens by asking how I'd describe my mission today, since "builder" doesn't quite cover what I do anymore between advising, educating, and writing. That question sets up the real spine of this conversation. What fraud strategy actually means, why I think predictive fraud analytics is mostly a fabrication, and how a fraud strategist's real job is making sure a team can react well when things break, not pretending anyone can stay ahead of fraudsters.

Hailey and I talk about where healthy skepticism turns into a blind spot, why foundation models are proving more valuable for enriching data than for scoring transactions, and I close out with real advice for anyone quietly wondering whether they could go independent themselves.

If one of your AI agents had been degrading for six weeks would you know? Most people tell me no, and that’s the problem I’m digging into.

Drifting agents can generate outputs that look fine on the outside while quietly getting worse underneath. Earlier in this series I talked about the reaction cycle as the master KPI of fraud effectiveness, and how agentic AI can make that cycle dramatically faster. This time I want to answer the question that matters once you’ve deployed those agents. How do you know they’re still working.

Most dashboards answer the wrong questions and only answer whether an agent is running. AI agent monitoring means tracking an agent deliberately, and I will walk you through exactly how to do it.

• Why a degrading agent is genuinely more dangerous than no agent at all.
• How to measure fraud reaction cycle speed at each individual stage rather than just watching one lagging number.
• Why AI agent performance metrics fraud teams should track don’t need to be perfectly automated to be useful.
• The difference between human-in-the-loop agent monitoring and autonomous agent monitoring for agents making decisions at scale.
• What a rising rejection rate actually tells you.
• How to catch a silently failing autonomous agent before real damage compounds.
• A practical three-layer AI agent monitoring dashboard fraud teams can build.

• Are running any agentic fraud ops monitoring program and want a real framework for catching a degrading agent before it shows up in your losses.
• Are responsible for AI agent governance fraud policies and need language that connects technical monitoring to leadership reporting.
• Have deployed human-in-the-loop tools like investigation copilots or rule recommendation agents and want to know what to actually track.
• Are running autonomous agents, like auto-labeling or alert clustering, with no human reviewing every decision, and worry about silent failure.
• Want to build a genuine business case for AI agent ROI fraud investment using the reaction cycle instead of just automation hours saved.

If you’ve been following fraud on LinkedIn for any real stretch of time, you are probably familiar with Brian Davis and have been reading his posts. He has been the first fraud hire at many companies across physical goods, e-commerce, marketplaces, and fintech. Today he sits at the center of it all running deep dive retreats through Safeguard.

I have talked with Brian before about AI adoption for fraud and risk teams, and he said something that stuck with me. There’s a real difference between AI activation and AI enablement. Most organizations think they’ve done the second when really they’ve only done the first. I wanted to bring our conversation to all of you, because I think fraud teams may underestimate or overestimate where they sit on this journey.

• Why AI activation vs AI enablement is the distinction most companies get wrong, and what it actually looks like when you throw a tool over the fence with no guidance.
• Brian's four pillars for real AI enablement are clear policies, actual training, dedicated incentives and time, and a feedback loop that doesn't die after three weeks.
• Why change management for fraud teams is really a people management problem wearing a technology costume.
• Brian's full five-stage framework, covering AI blocked, AI aware, AI enabled, AI first, and AI native, and how to honestly assess where your own team sits.
• Why AI enablement leadership buy-in has to start at the top, and how executives showing their own AI usage removes the imposter syndrome holding everyone else back.
• Why so many fraud teams try to go big on day one, transaction monitoring, KYC, and end up frustrated, when the smarter path is workflow design for AI adoption that starts small.
• Concrete, non-technical AI use cases for fraud, including pattern analysis, internal reporting, and OKR alignment with sales and marketing.
• How reducing engineering dependency with AI is changing what fraud analyst upskilling with AI actually looks like day to day.
• Brian's personal framework for fraud practitioner AI use cases, including his own daily habits and how he thinks about build versus buy AI fraud tools.

• Are a fraud or risk leader trying to figure out whether your team is actually AI enabled or just AI activated
• Are responsible for fraud team AI training or building out fraud team AI governance policies from scratch
• Are a fraud analyst wondering how to build AI literacy without waiting for your company to hand you a roadmap
• Are trying to motivate a team through fraud team change management without losing the people who are cautious about the change
• Are comparing AI first fraud organizations against the fully rebuilt AI native fraud teams and wondering which one is actually the realistic goal