The objective of this project was to analyze bank loan application data to uncover patterns in customer profiles, loan approvals, and repayment behavior.
This helps the bank in decision-making, risk analysis, and customer targeting.
Banks often face challenges in managing loan portfolios due to defaults and high-risk customers. The goal of this project is to:
-
Analyze loan data to track performance.
-
Identify good vs. bad loans.
-
Provide actionable insights for risk mitigation and business growth.
- SQL Server → Data cleaning, transformations, queries.
- Power BI → Dashboard design, KPI tracking, interactive visualizations.
- Excel → Initial validation & preprocessing.
- Data Analysis → Business insights & recommendations.
-
SQL Data Cleaning & Analysis
- Imported raw CSV into SQL Server.
- Cleaned and standardized data (dates, nulls, duplicates).
- Wrote queries to calculate:
- Loan approval rates
- Monthly loan applications
- Loan purpose distribution
- Default/charge-off rates
- Matched SQL outputs with Power BI dashboards to validate results.
-
Power BI Dashboard
- KPIs: Total Loan Applications, Total Funded Amount, Total Received Amount, Interest Rate, DTI(Debt to Income) Rate.
- Charts:
- Loan Status Distribution
- Loan Purpose Analysis
- Borrower Demographics (income, home ownership, employment length)
- Monthly Loan Trends
- Interactive filters for state, loan purpose, and loan grade.
✅ 86.2% loans are good, while 13.8% are bad (charged-off).
📉 60-month loans have higher default rates compared to 36-month loans.
💰 Debt consolidation is the most common loan purpose (~18K loans) but also carries the highest default risk.
📊 Charged-off loans carry higher average interest rates (15.1%), while fully paid loans average 11.6%.
🏡 Borrowers who own homes are the safest, while renters/mortgage holders are riskier.
📅 Loan applications peak in Nov–Dec, indicating seasonal borrowing patterns
💰 Debt consolidation and personal loans were most common.
🏡 Loan applications showed seasonality with peaks in specific months.
-
Apply stricter credit checks for Grades E, F, G and 60-month loans.
-
Encourage borrowers to opt for 36-month loans with slightly lower rates.
-
Focus marketing efforts on stable borrowers (homeowners, 10+ years of employment).
-
Create special loan products for debt consolidation with risk safeguards.
-
Use predictive analytics to identify high DTI & high-interest applicants at risk of default.
-
Adjust loan policies around seasonal peaks (Nov–Dec) to balance demand and risk
This analysis enables the bank to:
- Reduce default risk with better credit checks.
- Optimize loan approval strategy by focusing on reliable borrower segments.
- Plan cash flows by tracking seasonal loan demand.
- Document with insights & solutions.
- Power BI dashboard screenshot.
- README.md → Project summary (this file).
- Power BI file
-
Strengthened SQL skills for real-world data cleaning & analysis.
-
Built interactive dashboards in Power BI for business decision-making.
-
Understood loan risk patterns and customer segmentation strategies.
👤 [Sajjad Ali]
🔗 [linkedin.com/in/sajjad-ali-732279212] | [github.com/Sajjad-Ali-1411]