The Bank Loan Analytics Dashboard is an end-to-end data analytics project designed to analyze bank loan data and evaluate loan performance, repayment behavior, and credit risk.
The project uses SQL Server for data analysis and Power BI for interactive visualization, enabling stakeholders to monitor key loan metrics, compare good and bad loans, analyze trends over time, and drill down into individual loan records.
- Analyze overall loan applications and funding performance
- Evaluate repayment behavior and loan quality
- Identify patterns in good vs bad loans
- Track monthly trends in loan applications and disbursements
- Enable loan-level analysis using drill-through functionality
- SQL Server – Data querying, aggregations, KPI calculations
- Power BI – Interactive dashboards, DAX measures, drill-through
- Microsoft Excel – Initial data exploration and validation
SQL was used to perform structured analysis and calculate key business metrics, including:
-
Key KPIs
- Total Loan Applications
- Total Funded Amount
- Total Amount Received
- Average Interest Rate
- Average Debt-to-Income (DTI)
-
Time-Based Analysis
- Month-to-Date (MTD) and Previous Month-to-Date (PMTD) comparisons
- Monthly loan application trends
-
Loan Quality Classification
- Good Loans: Fully Paid, Current
- Bad Loans: Charged Off
-
Dimensional Analysis
- Loan Status
- State
- Purpose
- Employment Length
- Loan Term
- Home Ownership
Shows high-level KPIs, good vs bad loan comparison, and monthly trends.
Provides insights by loan purpose, term, employment length, and home ownership.
Displays individual loan records and supports drill-through analysis.
Drill-through is implemented to allow users to:
- Right-click on summary visuals (e.g., loan status or category)
- Navigate to the Details page
- View loan-level records filtered based on the selected context
This feature bridges high-level insights with granular data exploration.
Bank-Loan-Analytics-SQL-PowerBI/ ├── sql/ │ └── bank_loan_analysis.sql ├── powerbi/ │ └── Bank_Loan_Report.pbix ├── images/ │ ├── summary.png │ ├── overview.png │ └── details.png └── README.md
- Majority of loan applications fall under good loan categories
- Debt consolidation is the most common loan purpose
- Loan applications show consistent monthly trends
- Charged-off loans generally have higher interest rates and DTI values
This project demonstrates a complete data analytics workflow, from SQL-based data analysis to interactive Power BI reporting. It highlights how analytics can support performance monitoring, risk assessment, and data-driven decision-making in the banking domain.
📬 Feel free to connect via GitHub or LinkedIn for feedback or collaboration.