JPMorgan Chase AI Engineer Case Interview: Designing a Real-Time Fraud Detection Platform

You will lead a 75‑minute, whiteboard‑first case centered on building a production‑grade, real‑time card‑transaction fraud detection platform at JPMorgan Chase operating across 100+ countries. The case mirrors how JPMorgan Chase evaluates AI engineers: pragmatic ML/system design under strict latency, resiliency, and control requirements, with clear communication to risk, compliance, and engineering stakeholders. What the case covers: 1) Problem framing (clarify success metrics and constraints): define business objective (reduce fraud loss while minimizing customer friction); target metrics (recall/precision or cost‑weighted expected loss; p95 scoring latency ≤ 50 ms; peak throughput ≥ 20k TPS); SLAs/SLOs; RTO/RPO expectations; data residency and regional routing. 2) End‑to‑end architecture: stream ingestion (e.g., Kafka/Kinesis), online/offline feature store with parity guarantees, stateless model‑serving tier with autoscaling and canary/blue‑green deploys, multi‑region active‑active design, idempotent event processing, schema/contract management, secrets and key management, and audit logging. 3) Modeling approach: candidate contrasts gradient‑boosted trees vs deep models (incl. embeddings/sequence models); cost‑sensitive thresholds; champion/challenger setup; feature engineering for device/merchant/graph signals; explainability at decision time (e.g., SHAP/feature attributions) to support investigator workflows; drift/stability monitoring and periodic retraining policy. 4) Controls, risk, and governance aligned to JPMorgan Chase culture: model documentation (intended use, data lineage, conceptual soundness), validation and independent review, fairness/bias testing across protected classes, PII handling and data minimization, regional data residency, access controls and approvals, rollback/runbooks, and incident management. 5) Operational excellence and resilience: latency budget breakdown (featurization vs network vs inference), circuit breakers and graceful degradation paths, rate limiting, backpressure, feature backfill and time‑travel for reproducibility, shadow testing, and phased rollout. 6) Communication and trade‑offs: present a crisp architecture diagram, call out risks/assumptions, and explain choices to both an engineering peer and a risk/controls stakeholder. Typical flow: • 10 min—Requirements and clarifying questions • 25 min—Architecture and data/feature design • 15 min—Modeling and evaluation plan • 10 min—Risk/governance and monitoring • 10 min—Deep‑dive follow‑ups (e.g., false‑positive reduction strategy, regional failover, adding document/NLP or graph features) and Q&A. Deliverables during the session: an end‑to‑end diagram, a latency/throughput plan, a monitoring dashboard outline (business + ML + platform metrics), and a minimal rollout plan (champion/challenger, canary, success criteria). Interviewers score on systems thinking, production ML craft, controls mindset, clarity, and stakeholder alignment—hallmarks of JPMorgan Chase’s interview style.