This one hit different because evaluation without labels is genuinely hard and I spent too long on the modeling side before they nudged me toward the second half.
Start by acknowledging the lack of labels and the need for unsupervised anomaly detection. Propose a combination of methods like isolation forests, autoencoders, and clustering, and emphasize evaluation via proxy metrics, domain validation, and stability checks. Conclude with a plan for iterative refinement and potential semi-supervised steps.
Pro tip: Frame the solution as a ranking problem rather than a binary classification, and propose a human-in-the-loop system where investigators provide feedback to gradually create labels. This shows practical understanding of real-world fraud detection at scale.
Explore transaction data, identify features like amount, frequency, merchant, location, and time. Clarify what 'suspicious' means to PayPal and how alerts will be used.
Choose algorithms suited for high-dimensional, large-scale data: isolation forests, autoencoders, clustering (e.g., DBSCAN), and one-class SVM. Consider ensemble approaches to improve robustness.
Use proxy metrics: stability of anomalies over time, domain expert review of top-ranked anomalies, and internal consistency checks. Simulate known fraud patterns if available.
Set up a feedback loop where investigators label flagged cases, gradually creating a labeled dataset for semi-supervised learning. Monitor model drift and retrain periodically.
Deploy as a ranking system to prioritize investigations. Track metrics like precision@k, alert volume, and investigator feedback to refine the model.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.