Credit Card Transaction Analytics Dashboard

Overview

End-to-end data analytics project analyzing 10,000+ credit card transactions using PostgreSQL for data storage and querying, and Power BI for interactive visualization.

Tools Used

• PostgreSQL 18 — Database storage
• pgAdmin 4 — SQL query execution
• Power BI Desktop — Dashboard and visualization
• GitHub — Version control and portfolio

Dataset

• Source: Kaggle — Credit Card Financial Dashboard Dataset
• Records: 10,108 transactions + 10,108 customer records
• Period: Full year 2023 (52 weeks)

Dashboard Pages

1. Transaction Overview — Revenue KPIs, monthly trends, card category analysis
2. Customer Behavior — Job type, education, income group, gender analysis
3. Fraud & Risk Flags — Delinquent accounts, high risk customers, utilization
4. Weekly KPI Trends — Week on week revenue, transaction volume, interest

Key Insights

• Blue card generates ~$44M out of $55.32M total — 80% of all revenue
• Businessmen are the highest spending customer segment by job type
• Graduate educated customers spend the most by education level
• High income group dominates revenue vs middle and low income groups
• Swipe transactions generate more revenue than Chip or Online combined
• 614 delinquent accounts identified with Blue card having highest risk
• Week on Week revenue declined by 12.83% in the most recent week

SQL Techniques Used

• CREATE TABLE and CSV data import
• SELECT, WHERE, GROUP BY, ORDER BY, HAVING
• JOIN across two tables (credit_card + customer)
• CASE WHEN for customer segmentation
• CTE (WITH clause) for multi-step queries
• Window function LAG() for week on week revenue change

Screenshots