Design and evaluate a fraud detection strategy

## Context You are interviewing for a **Fraud Data Scientist** role at a payments company. The company has a fraud model and some operational constraints. ## Part A — Fraud domain knowledge 1. What are common **fraud types** in payments? Give examples. 2. What is **Account Takeover (ATO)** and how does it typically happen end-to-end? 3. Explain **first‑party vs. third‑party fraud** and how they differ in incentives, signals, and labeling. ## Part B — Model evaluation You have a fraud model that outputs a risk score per transaction. 1. Which metrics would you use to measure model quality (e.g., precision/recall/ROC‑AUC/PR‑AUC/cost)? 2. How would you choose a production threshold when false positives create customer friction? 3. What pitfalls exist in evaluating fraud models (delayed labels, selection bias, feedback loops, changing base rates, etc.)? ## Part C — Strategy from scratch Describe how you would design a **fraud strategy from 0** given: - You can use a combination of **rules + model scores + manual review**. - Review capacity is limited. - The business cares about both **fraud loss** and **customer experience**. ## Part D — Case prompt A one‑pager summary says: - Current system intercepts only **~40% of fraud** (low fraud capture). - Fraud loss is high. - A large share of fraud is coming from **emerging regions**. - You have **limited resources** to ship large engineering changes. - If the strategy causes **very low precision** (example: precision drops toward ~2% on blocked/flagged events), **complaints will increase**. **Task:** Propose a practical, staged plan to reduce fraud loss. Include: - Primary metric(s), diagnostic metric(s), and guardrails. - How you would segment (regions, customer types, payment methods, etc.). - What actions you would take (thresholding, rules, step‑up auth, review routing). - How you would validate impact and monitor after launch.