Project Title

Comprehensive Bank Loan Analysis Using SQL and Power BI

Project Overview

This project delves into a detailed analysis of bank loan data, utilizing SQL for data querying and manipulation, followed by visualization and further analysis in Power BI. The primary goal is to uncover insights related to loan applications, funding, repayments, and borrower demographics. The analysis spans various dimensions, including temporal (month, term), geographical (state), and categorical aspects (purpose, home ownership).

Objectives

• To provide a holistic view of the bank's loan portfolio performance.
• To identify trends and patterns in loan applications, approval rates, and repayment statuses.
• To assess financial health through KPIs such as Total Funded Amount, Average Interest Rate, and Loan Status.
• To facilitate strategic decision-making for the bank's credit and loan offerings.

Data Source

The project is based on a comprehensive dataset stored in SQL Server, encompassing various aspects of bank loans, including loan amounts, issue dates, interest rates, DTI ratios, and loan statuses.

Methodology

• Database Creation and Data Ingestion: A relational database was created in SQL Management Server to store loan data.
• Data Analysis and Query Writing: SQL queries were written to extract key performance indicators (KPIs), including total and monthly loan applications, funded amounts, average interest rates, and loan status breakdowns.
• Categorization of Loans: Loans were categorized as 'Good' or 'Bad' based on their repayment status, with further analysis on funded amounts and payments received.
• Temporal and Categorical Insights: Analysis was performed based on the issue month, state, loan term, employee length, loan purpose, and home ownership. Comparison with Power BI: The SQL query outputs were then compared with Power BI visualizations to ensure data consistency and to provide a graphical representation of the findings.

Key Insights and Findings

• A breakdown of the total loan applications and the distinction between MTD (Month-To-Date) and PMTD (Previous Month-To-Date) applications.
• Analysis of the total funded amount versus amounts received, offering insights into the bank's liquidity and loan performance.
• The average interest rate and DTI (Debt to Income) ratio analysis to understand the financial health of the borrowers.
• Segmentation of loans into 'Good' and 'Bad' categories, providing a clear picture of the loan portfolio's risk profile.
• Detailed breakdowns by loan status, purpose, state, term, and other factors to identify patterns and trends.

Tools and Technologies Used

• SQL Management Server: For database management and data analysis.
• Power BI: For data visualization and dashboard creation.

Future Work

Suggestions for future analyses could include predictive modeling to forecast loan defaults, demographic analysis to tailor loan products, or a deeper dive into the impact of loan terms on repayment rates.

Conclusion

This project offers valuable insights into the bank's loan portfolio, highlighting areas of strength and opportunities for improvement. It serves as a robust model for data-driven decision-making in financial services.