fraud detection

The classic Palantir decomposition prompt. The winning answer hinges on one asymmetry candidates rarely surface unprompted, and on starting with something embarrassingly simpler than an ML model.

Fraud is well under 1% of transactions, the labels arrive weeks late as chargebacks, and the fraudsters adapt to your model on purpose. Optimizing accuracy gives you a model that approves everything. The interview is dollars-at-risk thresholding, label delay, and a review queue with a fixed headcount.