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

I first came across Assaf Kipnis through his work online, and more recently he shared a post about realizing he was more than an investigator.

Assaf has spent 13 years in security, trust and safety, and fraud, with investigation roles at LinkedIn, Facebook, Google, and ElevenLabs. Today he runs his own consultancy focused on investigations, AI builds, and threat intelligence. In our conversation, he raises a question I hadn’t considered. While we as an industry are debating whether AI will replace fraud investigators, the people committing fraud are using AI to experiment, innovate, and improve their operations. What happens if we remove the very people who help us stay ahead?

We talk about leveraging AI for fraud investigations in a way investigators can actually trust. That includes the AI fraud investigation tools that are being built, why context matters more than hallucinations, what human in the loop really means, and how a fraud knowledge base can keep institutional knowledge from walking out the door. Let’s get into it.

• Why modern fraud operations run like companies, with goals, leaders, and recruiting, and why taking down a thousand accounts doesn’t slow them
• Why replacing fraud investigators with AI could become a competitive disadvantage for fraud teams
• How Assaf built an open source investigation tool that connects OSINT sources and saves evidence to disk
• Why hallucinations are less of a risk than AI making decisions with incomplete or outdated context
• What human in the loop and human on the loop mean in practice, and why reviewing every AI decision doesn’t scale
• Why most of an AI build should be deterministic code, and where a language model earns its place
• How an investigator’s adversarial mindset helps when building with AI, including treating the model like a threat actor
• How a fraud knowledge base captures what experienced investigators know and keeps it after they leave
• What fraud leaders should do instead of bolting AI onto existing processes

• Wonder whether AI will replace fraud investigators, or have been asked to automate investigations
• Lead a fraud team and want a practical path for an AI transformation that keeps your expertise
• Are an investigator or analyst who feels capped by not writing code and want to see what changes with AI
• Want to learn how to build AI fraud investigation tools with guardrails and traceable evidence
• Worry about what happens to your team’s knowledge when experienced people leave