This project focuses on detecting fraudulent credit card transactions by analyzing transaction patterns and anomalies. The goal is to help financial institutions identify suspicious activities and reduce financial losses using data-driven insights.
SecureGuard Financial Solutions provides fraud detection analytics for the financial industry. With the rise of digital payments, detecting unauthorized and unusual transactions has become critical for maintaining customer trust and minimizing fraud risk.
- Identify fraudulent and suspicious transactions
- Analyze spending patterns across categories, gender, and locations
- Perform exploratory data analysis to uncover anomalies
- Visualize fraud trends and geographical distribution
- Excel – Data exploration, summaries, and reports
- SQL – Data loading, joins, aggregations, fraud metrics
- Python – Exploratory Data Analysis (EDA)
- Tableau – Interactive dashboards and visual storytelling
- Statistical summary of transaction amount and city population
- Fraud analysis by gender and category
- Top states by transaction volume
- Correlation analysis between transaction amount and city population
- Schema and table creation
- Fraud percentage calculation
- Category-wise and gender-wise analysis
- Location-based analysis using joins
- Time-based transaction insights
- Data quality and missing value checks
- Distribution and outlier analysis
- Correlation analysis
- Fraud vs non-fraud comparison
- Trend analysis over time
- Box & whisker plots by gender and category
- Geographical maps using latitude and longitude
- Fraud distribution maps
- Monthly transaction trends
- Inflation-adjusted transaction analysis dashboard
The analysis successfully identified patterns associated with fraudulent transactions, highlighted high-risk categories, and provided actionable insights through interactive visualizations.
The dataset contains anonymized credit card transaction records and location information for analysis purposes.
Ali Hamza