JPMorgan Chase Data Analyst Case Interview — Payments & Fraud Analytics Scenario

This case mirrors a common JPMorgan Chase (JPMC) analyst interview format: a time‑boxed, business-first analytics problem grounded in retail banking/payments, with an emphasis on controls, auditability, and clear executive communication. You will work in a collaborative doc/whiteboard while talking through your approach; expect probing on data quality, stakeholder trade‑offs, and how you’d operationalize insights in a highly regulated environment. Scenario: You are a Data Analyst embedded in the Consumer & Community Banking organization. Recent spikes in card‑not‑present fraud are pressuring loss rates and customer experience. A product manager wants to tighten rules to reduce fraud, while Merchant Services pushes to protect authorization rates. Using a sample dataset, recommend a course of action, quantify impact, and outline the controls needed to deploy safely. Data context (simplified tables): - transactions(tx_id, cust_id, merchant_id, channel, amount, currency, auth_result, timestamp) - chargebacks(tx_id, reason_code, cb_timestamp) - customers(cust_id, segment, tenure_months, risk_band, state) - merchants(merchant_id, mcc, region, risk_tier) What the interview covers (and how JPMC evaluates you): 1) Clarify & frame (5–7 min): Translate the objective into measurable KPIs (e.g., approval rate, fraud rate in bps, chargeback ratio, customer friction). State assumptions and success criteria; call out regulatory and customer‑impact lenses. 2) Data quality & controls (5–8 min): Describe checks for duplicates, timestamp gaps, currency normalization, and PII handling. Explain data lineage, reproducibility, and how you’d maintain an audit trail for Model Risk/Control reviews. 3) Core SQL/analysis (12–15 min): Write or pseudocode queries to: (a) compute fraud and approval rates by channel/MCC; (b) identify merchants with rising fraud trends; (c) estimate false‑positive impact if a new rule blocks high‑risk segments. Discuss indexing/partitioning and join strategy on large tables. 4) Business insight & trade‑offs (8–10 min): Interpret results, segment customers/merchants, and propose targeted mitigations (e.g., step‑up auth only for high‑risk MCC×channel cohorts). Quantify expected lift and the cost of customer friction; show back‑of‑the‑envelope math to translate basis points into monthly losses/savings. 5) Experiment/design (5–7 min): Outline an A/B or phased rollout (guardrail metrics: approval rate, fraud bps, dispute rate, CSAT proxies). Define monitoring, alert thresholds, and rollback criteria consistent with JPMC’s risk posture. 6) Visualization & storytelling (5–7 min): Describe a concise Tableau/Power BI view for senior stakeholders: trend lines, cohort heatmaps, and an exec readout summarizing recommendation, risk, and next steps in plain language. 7) Operationalization & governance (3–5 min): Explain how you’d productionize (data SLAs, automated QA, controls sign‑offs, periodic model review) and partner with Product, Risk, Fraud Ops, and Tech. Culture & style fit the firm: client‑obsessed recommendations, ownership mindset, crisp communication to mixed audiences, and an explicit controls lens. Depth in SQL and pragmatic analytics matters, but so do judgment, stakeholder management, and the ability to document assumptions for audit/readiness. What to prepare: be ready to write SQL, reason with incomplete data, quantify impact in dollars/bps, and defend a rollout plan that balances fraud loss vs. authorization rate under regulatory scrutiny.