Your AI System Passed the Audit, So Why Is It Still a Fraud Risk?

Most organisations begin their AI journey by asking questions about the technology. Which tools? Which platforms? How do we keep up? Those are fair questions. But I’d suggest these aren’t the first set we should be asking.

Before we reach for the technology, I think we have to sit with something more basic: how does a decision actually get made here? Who owns it? Who checks it? And if it goes wrong, what was ever there to catch it? Those questions were with us long before AI arrived, and they’ll still be with us long after today’s technology has been replaced by something newer.

This session sits with exactly those questions. We’ll look at where AI risk actually lives: in the data, the assumptions, the outputs we accept without pausing, and the oversight that tends to arrive only after a loss.

Drawing on real cases, from a USD25 million deepfake to failures closer to home, attendees will see how AI has changed the texture and the tempo of fraud, and not its fundamentals.

You’ll leave with a set of practical questions you can bring to any AI-assisted decision, a clearer sense of the early warning signs worth watching for, and a way of thinking about your role that moves it a little further upstream. Less about investigating losses after they happen, and more about preventing them before they do.