Fraud model: build or buy?
Data gravity and real-time latency pin it to self-host.
Open the live lab · pre-loaded to this scenario
Build vs Buy vs Fine-Tune
Context
A card issuer decides how to run its fraud model. A vendor is fast to stand up, but the training data can't leave the bank, scoring must happen in real time, and the model itself is a competitive edge.
The decision
Self-host / fine-tune wins here — data gravity, sub-100ms latency, and differentiation all point the same way, and the bank has the team to run it.
What most miss
The vendor's time-to-value is seductive, but on fraud the data can't move and the latency is non-negotiable — control isn't a preference, it's a constraint.
Stakes
A vendor round-trip that adds latency or moves fraud data off-prem isn't a cost line — it's a regulatory and loss-rate problem.
First-hand · Business of AI · verified 2026-07-03
Sources: Fraud-model build/buy — first-hand (cards & payments, American Express); Real-time scoring + data-gravity constraints