Retail Fraud Detection Analysis Using SQL with Ravi Kumar Gupta

🚨 Retail Fraud Detection Analysis Using SQL

This project focuses on analyzing retail transaction data to identify fraud patterns, suspicious customers, risky payment methods, and unusual transaction behavior using SQL.

The analysis was performed on a real-world style dataset containing 100,000+ transaction records. Advanced SQL concepts were used to generate meaningful business insights and fraud detection reports.

🔹 Key Analysis Performed:

✔ Fraud Transaction Analysis

✔ Fraud Percentage by Merchant Category

✔ Payment Method Fraud Rate

✔ High Risk Device Analysis

✔ Day-wise Fraud Trend

✔ Monthly Fraud Growth Rate

✔ Suspicious Customer Detection

✔ Running Fraud Total using Window Functions

✔ Top Risky Customers using CTE

✔ Fraud Ranking using DENSE_RANK()

🛠️ Skills & Technologies:

✔ SQL

✔ MySQL

✔ Window Functions

✔ CASE Statement

✔ CTE

✔ Aggregate Functions

✔ Ranking Functions

✔ Data Analysis

✔ Fraud Detection

📊 Business Goal:

The objective of this project is to help businesses identify fraud trends, reduce financial losses, and improve transaction security using SQL analytics.