Credit Card Transaction Analysis

Financial institutions need to understand customer behavior based on transaction data to optimize services and manage risk. This project aimed to analyze credit card transactions to:

• Identify common spending categories and patterns among cardholders.
• Segment customers based on their transaction behavior (e.g., high spenders, frequent travelers, specific merchant preferences).
• Uncover potential indicators of fraudulent activity or credit risk.
• Visualize these findings in an interactive dashboard for stakeholders in marketing, risk management, and product development.

SQL was used for data extraction, cleaning, and aggregation from the transaction database (over 10k records). Power BI was then employed to build an interactive dashboard. Key steps and insights included:

• Performed data wrangling and feature engineering using SQL queries to calculate metrics like transaction frequency, average transaction value, and spending by category.
• Developed customer segments using RFM (Recency, Frequency, Monetary) analysis principles adapted for transaction data.
• Identified correlations between demographic data (if available) and spending habits.
• Analyzed transaction types and amounts to flag potential anomalies or high-risk patterns (e.g., unusually large purchases, rapid succession transactions).
• The Power BI dashboard allowed users to filter by time period, customer segment, spending category, and location to explore patterns interactively.
• Visualized key metrics such as total spending, top merchant categories, spending distribution across segments, and geographic spending patterns.

The analysis provided valuable insights into customer spending behavior and segmentation. The Power BI dashboard serves as a dynamic tool for various departments. Marketing can use segment insights for targeted campaigns, risk management can monitor potential fraud indicators, and product development can identify popular spending categories for potential partnerships or card features. By leveraging SQL for data processing and Power BI for visualization, this project delivered actionable intelligence from raw transaction data, enabling more informed strategic decisions for the credit card issuer.