Wells Fargo Data Analyst Case: Deposit Fee Variance and Customer Impact Analysis

This case mirrors real Wells Fargo data analyst interviews that blend hands-on analytics with risk-and-controls thinking and stakeholder communication. You are placed on a cross-functional team (Product, Branch Banking Analytics, and Independent Risk) investigating an unexpected rise in overdraft/NSF fees across several regions. The interviewer provides a masked, sample dataset (transactions at the account level; fee assessments; branch/product attributes; daily balances; complaint tickets) plus a short email thread from a regional leader and a risk partner. What you’ll do: 1) Frame the problem and define success: propose measurable KPIs (e.g., fee incidence per 1,000 accounts, fee $/active account, complaint rate per 10k accounts), segmentation (region, branch, product, channel), and a baseline/seasonality check. 2) Data wrangling and SQL: outline or write sample SQL/pseudocode to clean and join tables (window functions for running balances and fee sequences; time-of-day distribution checks to detect posting-order shifts; left joins to complaints and branch attributes; outlier handling). 3) Analysis and hypotheses: quantify where and when the variance occurs; test plausible drivers (batch posting delay, product policy change, channel mix shift to ACH, specific branch behaviors, fee waiver process gaps). 4) Risk, compliance, and data governance: call out PII handling (masked IDs), data lineage assumptions, control gaps, and how you’d partner with the three lines of defense; suggest fair and consistent treatment checks (e.g., compare fee patterns across customer segments without using prohibited attributes; propose proxy-safe monitoring). 5) Communication: synthesize findings for two audiences—an exec summary for a regional business leader and a risk memo-style note identifying issue severity, customer impact, and monitoring/closure steps. 6) Recommendations: propose near-term controls (alert thresholds, dashboard with daily variance, automated exception report) and longer-term fixes (policy clarifications, posting schedule adjustments, branch training, A/B testing of waiver criteria, instrumentation for better metadata capture). Evaluation rubric (typical of Wells Fargo): structured problem solving (30%), SQL/data munging accuracy and clarity (30%), risk-and-controls mindset and documentation readiness (20%), stakeholder communication and practicality of recommendations (20%). Format and timing typical of Wells Fargo case rounds: 5 min brief, 35–40 min analysis/whiteboarding (SQL and reasoning), 10–15 min readout and Q&A with follow-ups from a risk partner. Tools and techniques expected: SQL (Teradata/Oracle/Hive-like syntax), basic statistics for trend and seasonality checks, clear assumptions, and concise executive communication aligned to Wells Fargo’s customer-first and risk excellence culture.