This project analyzes bank loan data using SQL and Power BI to track key financial trends, loan application patterns, and bank profitability. The SQL queries perform exploratory data analysis (EDA), while Power BI visualizes key performance indicators (KPIs) for better decision-making.
The SQL file includes various queries to analyze:
Total Loan Applications: Count of all loan applications.
Month-to-Date (MTD) Loan Applications: Loan applications for a specific month and year.
Loan Application Percentage: Monthly share of total loan applications.
Growth Rate Analysis: Month-over-month changes in loan applications.
Profitability Analysis: Calculating net profit from loans based on payments and loan amounts.
Power BI Dashboard
The Power BI report provides interactive visualizations and insights, including:
Loan Approval Rate
Default Rate
Total Revenue from Loans
Net Profit Trends
Time series charts showing loan trends
Profitability breakdowns by customer segments
Monthly comparisons of loan performance
How to Use
SQL Analysis: Run the queries in a PostgreSQL or similar database to extract insights.
Power BI Dashboard: Import the dataset into Power BI and explore the interactive reports.
Conclusion
This project provides a data-driven approach to understanding bank loan performance and profitability. The combination of SQL and Power BI enables efficient financial analysis and decision-making.
1- Bank Loan Data - csv File 2- Terminologies used in the Data - Word File 3- EDA - SQL File 4- PowerBI Dashboard - pbix