Retail Fraud Analysis Retail businesses face a tough balancing act: missing real fraud drains rev...Retail Fraud Analysis Retail businesses face a tough balancing act: missing real fraud drains rev...
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Retail Fraud Analysis
Retail businesses face a tough balancing act: missing real fraud drains revenue, while overly aggressive security flags legitimate customers and hurts retention.
To solve this, I built an end-to-end analytics project examining 100,000 retail transactions to uncover where risk actually concentrates across payment methods, merchant categories, and locations.
The Data Challenge & Solution Deep data profiling revealed the native fraud-risk score was completely broken (flatlined at 0.0 across all records), and two location fields were 100% redundant. Instead of faking the data, I engineered a transparent, custom Risk Level classification (Low, Medium, High) using real behavioral security flags already present in the dataset, like velocity and high-risk devices.
Technical Execution
SQL: Handled data cleaning, removed duplicate records, standardized formatting, and resolved null values.
Power BI: Built an interactive 3-page dashboard featuring an Executive Overview, Fraud Analysis, and Risk & Recommendations.
Key Findings
Unexpected Exposure: Medium Risk transactions carried the highest financial exposure ($7.1M) ahead of High Risk ($3.3M), signaling a need to recalibrate risk thresholds.
Transaction Trends: Fraud clustered heavily in lower brackets ($0–$500) rather than large sums, with domestic transactions making up 66.8% of activity.
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