Visa Data Analyst Case Interview – Payments Authorization and Fraud Trade-off

This Visa case interview simulates a real client analytics engagement on VisaNet: improving authorization rates for a global e-commerce merchant while keeping fraud within acceptable basis points. It mirrors Visa's data-driven, client-first culture and emphasis on network reliability, risk controls, and clear stakeholder communication. What you will do: - Frame the problem: baseline the merchant's approval rate and fraud metrics, identify drivers of false declines, and quantify potential uplift from targeted interventions such as 3-D Secure, tokenization, velocity rules, or issuer outreach. - Work with realistic payments data and constraints: you will write SQL and reason with pivot-style outputs to segment performance by issuer BIN, acquirer, MCC, country pair, channel, cross‑border flag, tokenized flag, and 3DS usage, accounting for FX and time windows. - Define and use core network metrics: - Authorization or approval rate = approved authorizations / total attempts - False decline rate = customer-intended good transactions declined / total attempts (you will infer using proxies such as subsequent success and low risk scores) - Fraud rate in basis points = confirmed fraud losses / sales volume × 10,000 - Chargeback rate = chargebacks count / transactions count - Dispute win rate and time to resolution for operational impact - Execute targeted analyses (SQL-heavy, Visa style): - Identify top decline reason codes and where do_not_honor and generic issuer declines cluster by issuer BIN, region, and MCC. - Cohort analysis of approval rate before vs after tokenization or 3DS adoption; compute FX‑normalized lift. - Root-cause view: acquirer vs issuer contribution to declines; weekend and hour-of-day spikes; cross‑border vs domestic patterns. - Quick experiment read: estimate the impact of moving high-risk cohorts to step-up 3DS while keeping friction low for trusted tokens. - Quantify business and risk impact: estimate incremental approved sales, fraud loss delta, and net value for client and network health. Show trade-offs and guardrails aligned to Visa risk policies. - Communicate like a Visa analyst: translate findings into a short client-ready narrative with a prioritized action plan, risks and mitigations, and how to monitor post-launch. Data you receive (sample schema excerpt): - transactions: txn_id, card_id_hash, merchant_id, mcc, channel, txn_timestamp, amount_local, currency, exchange_rate_to_usd, country_merchant, country_card, cross_border_flag, tokenized_flag, three_ds_flag, auth_status, decline_reason, risk_score, issuer_bin, acquirer_id - disputes: chargeback_id, txn_id, reason_code, amount_usd, cycle, final_outcome, filed_date - experiment_assignments: card_id_hash, cohort, start_date Expected artifacts: - 4 to 6 targeted SQL queries or well-structured pseudo-SQL answering the prompts - A brief table or chart summarizing segment performance and projected lift - A 5 to 7 bullet recommendation plan with measurement plan and risk controls How you will be evaluated (Visa-aligned rubric): - Payments domain intuition: correctness of metric definitions, issuer vs acquirer reasoning, awareness of cross‑border nuances and FX effects - Analytical rigor and SQL quality: correct joins, filters, window functions, and defensible assumptions for proxies such as false declines - Risk and controls mindset: proposals that raise approvals without breaching fraud thresholds; attention to data privacy and PII minimization - Client orientation and communication: clear narrative, trade-off articulation, and pragmatic, testable recommendations - Insight to impact: ability to quantify uplift and outline a monitoring plan with leading indicators and success criteria Timing guide (typical Visa case): - 10 min: clarify goals, define metrics, align on constraints and success thresholds - 35 min: hands-on analysis and intermediate check-ins - 10 min: trade-off discussion and sensitivity testing - 10 min: executive-style readout and Q and A Common probes used by Visa interviewers: - How would your approach change for tokenized vs non-tokenized PANs, or when an issuer enforces SCA via 3DS? - If approval rates improve but chargebacks rise in specific MCCs, what guardrails and monitoring would you put in place? - Which 3 issuers would you contact first, and what evidence would you present to justify configuration changes? - How do you ensure FX does not bias cross‑border trend comparisons? This format reflects real Visa case expectations: practical SQL analysis on payments data, focus on authorization and fraud trade-offs, and a concise client-ready story.