Samuel Maina
Freelance Data Analyst | SQL, Excel, and Power BI Expert.
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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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Retail Inventory and Optimization Tool
Retailers often collect large amounts of sales and inventory data but lack
a system to turn it into clear, actionable decisions, making it hard to
know which products to reorder, which are slow-moving, and which risk
stockouts or overstocking.
I built an end-to-end analytics pipeline to solve this. Using Python and
Pandas, I cleaned a raw retail dataset, handling missing values based on
what each one actually meant in context (e.g. a missing discount treated
as "none," while a missing inventory value was filled with the average
rather than falsely assumed to be zero). The cleaned data was loaded into
MySQL for exploratory analysis, then connected to an interactive Power BI
dashboard summarizing revenue, stock levels, demand fulfillment, and sales
trends by category, store, and season.
Key findings included a 95.71% demand fulfillment rate (revealing real,
recoverable lost sales), evenly balanced sales across all products
categories and seasons, and one store carrying noticeably higher inventory
than others despite similar sales flagging a possible overstocking risk.
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Security Support Information System (SSIS)
The Security Support Information System (SSIS) is an end-to-end Business Intelligence solution developed to address operational visibility and decision-making challenges in the private security industry.
The project involved identifying a real business problem, cleaning and transforming operational datasets using Python (Pandas), designing a relational MySQL database, implementing SQL scripts, and developing interactive Power BI dashboards.
The final solution delivers Executive, Workforce, Operations, and Commercial Analytics dashboards featuring KPI reporting, DAX calculations, and interactive visualizations to help managers make informed, data-driven decisions.
Tools Used: Python (Pandas), MySQL, SQL, Microsoft Power BI, DAX, Excel.
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