Capital One AI Engineer Case Interview: Real-Time Fraud Detection System Design

This 60-minute, case-style session mirrors Capital One's analytical, customer-first, and test-and-learn culture found in real interview loops (including final-round "Power Day"). You will design an end-to-end AI/ML service that scores credit card transactions for fraud in real time, integrating model, data, and cloud production concerns under bank-grade risk and compliance constraints. What the interviewer provides (5 min): - Business goal: Reduce fraud losses while minimizing false declines and customer friction. - Context: Card authorization path must call your model service before approving/declining a transaction. - Guardrails: Latency SLA at p99 suitable for an auth call, high availability across regions, strong security/PII handling, and clear monitoring/rollback. What you are expected to do (primary discussion 40–45 min): 1) Clarify and frame the problem - Define success metrics (e.g., fraud loss rate, false positive rate/decline rate, approval rate, customer friction, business impact $). - Establish key non-functionals: throughput assumptions, p99 latency budget across the call chain, reliability targets, and peak/seasonality considerations. 2) Data and features - Identify streaming and batch sources: transaction stream, cardholder profile, merchant/device signals, historical aggregates. - Propose a feature store strategy (online + offline parity), point-in-time correctness, PII tokenization, data quality checks, and drift/outlier detection. 3) Model approach and responsible AI - Discuss candidate models (e.g., gradient-boosted trees vs deep models) and the explainability tradeoff for high-stakes decisions; outline how you would produce reason codes and use model-agnostic explainers. - Address fairness and bias testing, thresholds by segment, and how policies differ for fraud vs underwriting use cases (e.g., adverse action explainability for credit decisions). - Champion/challenger strategy; how you would document and review under model risk governance. 4) Production architecture on cloud (Capital One is cloud-forward) - Sketch a low-latency scoring path: API gateway/load balancer -> stateless inference service -> feature cache (e.g., Redis/DynamoDB) -> model endpoint -> decision/thresholding -> response. - Streaming ingestion and training loop: event bus/stream, data lake, offline training, model registry, CI/CD for models, canary and dark launch. - Observability: feature and prediction drift, data quality SLAs, model and business KPIs, and automated rollback. 5) Experimentation and test-and-learn - Propose A/B or bandit flighting at the authorization layer, power calculations, ramp plans, guardrails (customer impact, fraud loss caps), and how to read results. Deep-dive prompts the interviewer may ask (choose 2–3): - Handling cold start and sparse merchants, travel scenarios, or attack spikes. - Designing fallbacks when the model is unavailable or slow (graceful degradation, rules, risk bands). - Hardening against adversarial behavior and feature gaming. - Cost-performance tradeoffs under strict latency. Evaluation rubric (what “strong” looks like): - Problem framing and metrics: crisp success criteria, thoughtful tradeoffs, and clear assumptions. - ML/system design depth: sound model choice, data plan, and real-time architecture with latency/reliability in mind. - Responsible AI and governance: fairness, explainability, documentation, auditability, and rollback paths consistent with regulated finance. - Experimentation: practical test-and-learn plan with business impact thinking. - Communication and collaboration: structured, hypothesis-driven, invites clarifying questions, uses simple diagrams. Expected artifacts by the end: - A whiteboard/system diagram of the scoring and training loop. - Metric definitions and an initial KPI dashboard sketch. - A rollout plan (canary, success/fail criteria, rollback triggers) and a brief risk register. Behavioral overlay (woven throughout): - Examples that show customer-first thinking, ownership, simplicity, and partnership with risk/compliance. - Clear STAR responses when probed on production incidents, model failures, or cross-functional tradeoffs.