Fireside chat: AI-driven fraud attacks

What’s up fraud fighters, and welcome back to Fraud Forward!

This one is recorded live at SardineCon as a fireside chat. You’re getting the real, unscripted version of a conversation I had with Andrew Austin from Sardine and Arjun Ramakrishnan from GoDaddy. This one involves no rehearsed talking points. Just a conversation in front of a room full of people who live this work every day. Arjun brought the merchant and payments side. Andrew brought the fraud technology side. We sat down to talk about AI driven fraud, and how cheap and fast this has all become.

AI is not introducing a new species of fraud. It’s the same things we have always fought. Everything from bust-out schemes, card testing, synthetic identities, to fake businesses. What’s changed is the speed, the scale, and the cost. That’s what we spend the next forty-five minutes unpacking.

• How AI-generated fraudulent websites and website cloning fraud have erased one of the oldest fraud indicators fraud teams relied on. We can no longer just rely on whether a business’s website looks legitimate.
• How bust-out fraud has scaled from two or three manually cultivated accounts to AI agents fraud rings running 30 or 40 synthetic accounts simultaneously.
• Why card testing attacks no longer follow predictable dollar amounts or velocity patterns, and what that means for payment fraud velocity patterns detection.
• The real tension fraud teams face balancing KYB friction vs fraud, and why adding more friction can push legitimate customers to a competitor.
• Why consortium data fraud detection and network-level fraud analysis matter more than ever when a single fraud ring can span dozens of seemingly unrelated accounts.
• How cost asymmetry in fraud is reshaping the entire fight, where attackers iterate for cents while defenders spend real dollars to respond.
• Why traditional fraud loss forecasting models are becoming unreliable in a world of constantly evolving agentic fraud attacks.
• What fraud team innovation strategy actually needs to look like going forward, including real-time fraud detection, fraud rule engine automation, and a genuine embrace of AI fraud cost barrier realities.

• Work in merchant onboarding fraud risk, payments, or trust and safety and want a grounded, practitioner-level view of AI driven fraud.
• Are responsible for detecting synthetic identity fraud, card not present fraud, or bust-out fraud at a bank, fintech, or payments platform.
• Have ever had to defend a KYB friction vs fraud tradeoff to your sales or product team and want language to make that case better.
• Are evaluating consortium data fraud detection or network-level fraud analysis tools and want real-world context for why they matter now.
• Lead a fraud team trying to figure out where to actually invest given the AI fraud cost barrier attackers no longer face.
• Want a live conversation with real talk from people actively fighting this every day.