mastercard

Mastercard AI Engineer Case Interview — Real‑Time Fraud Decisioning and Responsible AI at Network Scale

This case simulates designing, deploying, and governing an ML‑driven risk decisioning service used in Mastercard’s payments network. It mirrors Mastercard’s culture (Decency Quotient, privacy‑by‑design, reliability at global scale) and interview style (structured problem framing, production mindset, responsible AI rigor). What the case covers: 1) Problem framing and objectives - Business goal: reduce fraud and false declines during authorization while preserving customer experience and interchange revenue. - Define cost‑sensitive metrics (e.g., expected loss, fraud capture, false positive rate, approval lift) and operating thresholds for issuers/merchants. - Clarify constraints typical of network participants (issuer, acquirer, merchant, wallet) and cross‑border nuances. 2) Data and features - Streaming and historical signals: transaction details (amount, MCC, country/currency), PAN tokenization signals, device/browser, merchant risk attributes, network velocity, chargeback labels, dispute outcomes, optional 3DS signals. - Feature strategy: time‑window aggregates, sequence features per PAN/token/device, graph features (card‑merchant‑device link risk), geospatial and timezone consistency checks. - Data quality: late/dirty events, deduplication, schema evolution, idempotency. 3) Privacy, security, and compliance (Mastercard emphasis) - PCI‑DSS scope minimization, tokenization first, encryption at rest/in transit, KMS/HSM usage. - PII handling, data residency, consent and purpose limitation; regional policy toggles. - Access controls, auditability, repeatable lineage; production data handling with least privilege. 4) Modeling approach - Candidate justifies a baseline (gradient‑boosted trees or calibrated logistic regression) plus an advanced approach (sequence model or graph model) and champion/challenger setup. - Class imbalance handling, calibration, thresholding by segment; explainability for issuer review (global + per‑decision reason codes). - Fairness checks aligned with Responsible AI: selection of appropriate fairness diagnostics, regional compliance considerations, and mitigation strategies. 5) System design and SLAs - Online scoring path with strict latency budget (e.g., ≤50 ms inference within an authorization flow), high availability, deterministic fallbacks (rules or cached scores) on degradation. - Architecture: streaming pipeline (e.g., Kafka/Flink‑like), feature store (offline/online parity), model registry, blue‑green/canary deploys, kill‑switch, rollback. - API design: /score endpoint with versioning, schema contracts, reason codes, and trace IDs for audit. 6) Experimentation, monitoring, and governance - A/B or multi‑armed bandit at issuer/merchant level; guardrails to cap customer impact. - Post‑deployment monitoring: drift, stability, calibration, business KPIs; alerting and incident response runbook. - Model Risk Management: documentation, validation, reproducibility, periodic review, and challenger rotation. 7) Collaboration and DQ (Decency Quotient) behaviors - Stakeholder alignment across banks, merchants, product, legal, and privacy. - Candidate articulates how decisions reflect Mastercard’s DQ (transparency to issuers, minimizing unintended harm, handling customer disputes fairly). Interview flow (template): - 5 min: prompt read + clarifying questions. - 25–30 min: end‑to‑end design (data, model, system, privacy/security) with diagramming. - 10–15 min: trade‑offs (latency vs. accuracy, fairness vs. performance, cost vs. coverage) and failure modes. - 10 min: responsible AI, MRM, explainability, and experiment plan. - 5 min: DQ/behavioral scenario (e.g., handling a spike in false declines for a vulnerable customer segment). - 5 min: candidate Q&A. Evaluation rubric (used by interviewers): - Clarity of problem framing and metric selection; ability to tie metrics to network‑level outcomes. - Sound modeling choices, feature strategy, and explainability rationale for issuer‑facing decisions. - Production‑grade system design meeting latency, availability, and fallback requirements. - Mastery of privacy, security, and compliance in a payments context. - Experimentation and monitoring plan with concrete guardrails. - DQ behaviors: empathy, transparency, and cross‑stakeholder collaboration. Deliverables expected from candidate during the session: - A concise architecture diagram of online/offline paths. - A prioritized metric stack and operating threshold proposal. - An outline of the governance/validation checklist and rollout plan.

engineering

8 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

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