So this episode starts with a data executive walking into a fraud team.
Which sounds like the setup to a very niche joke. And maybe it is. But honestly, it also describes a real problem most fraud teams know too well: we rely on data for almost everything, but the relationship between fraud teams and data teams is often messier than anyone wants to admit.
In this episode, I’m joined by Shachar Meir, a data advisor and former director of data at Meta, to talk about LLM analytics, self-service analytics, data infrastructure, and why fraud teams cannot just throw AI on top of messy data and hope it becomes strategy.
Because yes, LLM-powered self-service analytics sounds amazing. Ask a question in plain English, get an answer, move faster, avoid waiting three weeks for a data team with 900 priorities. Great. I want that world too.
But then you’ve got to ask yourself: where did the answer come from? Which tables did it join? What definition did it use? Did it understand the business context? Did it hallucinate? Did you ask the right question in the first place?
Not a small detail.
This conversation is really about the gap between the promise of AI analytics tools and the operational reality of fraud analytics. Fraud and risk teams work in adversarial environments. The problem keeps changing. The signal-to-noise ratio is bad. The cost of getting it wrong can be massive, whether that means letting fraud through or blocking legitimate users. So if you want LLM analytics to actually help, you need more than a shiny interface. You need data foundations, governance, semantic clarity, and the humility to start small.
What you’ll hear in this episode:
- Why fraud and risk teams are unusually complex data customers
- How fraud teams can work better with data teams instead of requesting endless point solutions
- Why self-service analytics failed so often before LLMs entered the picture
- What changes, and what does not change, when LLM analytics becomes the interface
- Why asking the right data question matters more than getting a fast answer
- How data governance, semantic layers, and data quality shape AI analytics results
- Why fraud teams should start AI adoption with one curated table, one use case, and one measurable outcome
- How to think about the one-hour, one-day, one-week, and one-month versions of a data project
You should listen to this episode if you:
- Work in fraud operations and depend on data teams to ship risk or fraud analytics projects
- Are considering LLM analytics or AI agents for data analysis inside your fraud stack
- Have been promised AI data querying that sounds too easy, because it probably is
- Need better ways to align with data engineering, analytics, or risk data infrastructure teams
- Want a practical way to think about AI data governance before deploying tools that affect real users
