This project is an interactive Power BI dashboard built to analyze bank loan portfolio performance. The dashboard focuses on monitoring key lending metrics such as loan applications, funded amount, amount received, average interest rate, debt-to-income ratio, and good/bad loan segmentation.
The main objective of this project is to practice building a finance-domain analytics dashboard that helps business users understand loan portfolio performance, repayment behavior, and potential credit risk patterns.
This is a guided portfolio project created for learning and practice purposes, with customization in KPI interpretation, dashboard structure, and business insight presentation.
The dashboard was designed to answer the following business questions:
- How many loan applications were received?
- What is the total funded amount?
- What is the total amount received from borrowers?
- What is the average interest rate?
- What is the average debt-to-income ratio?
- What percentage of loans are classified as good loans and bad loans?
- How does loan performance vary by state, loan term, employment length, loan purpose, and home ownership?
- Which loan segments may require closer monitoring?
The dataset contains historical bank loan records, including loan amount, funded amount, repayment amount, loan status, borrower information, loan purpose, loan grade, interest rate, debt-to-income ratio, and loan issue date.
Main fields used in the analysis include:
| Field | Description |
|---|---|
id |
Unique loan/application ID |
loan_status |
Current status of the loan |
loan_amount |
Loan amount requested or approved |
total_payment |
Total amount received from borrower |
int_rate |
Interest rate of the loan |
dti |
Debt-to-income ratio |
issue_date |
Loan issue date |
address_state |
Borrower's state |
term |
Loan repayment term |
emp_length |
Borrower's employment length |
purpose |
Purpose of the loan |
home_ownership |
Borrower's home ownership status |
grade |
Loan grade |
sub_grade |
Loan sub-grade |
- Power BI: Dashboard development and data visualization
- Power Query: Data cleaning and transformation
- DAX: KPI measures and calculated metrics
- Data Visualization: KPI cards, charts, slicers, and interactive report pages
- Business Analysis: Loan portfolio monitoring and risk-related insight generation
The Power BI report includes three main pages:
The Summary page provides an executive-level overview of the bank loan portfolio.
Main metrics include:
- Total Loan Applications
- Total Funded Amount
- Total Amount Received
- Average Interest Rate
- Average Debt-to-Income Ratio
- Good Loan Percentage
- Bad Loan Percentage
- Loan Status Breakdown
- Month-to-Date and Month-over-Month performance
This page helps users quickly evaluate overall lending performance and portfolio quality.
The Overview page provides a deeper breakdown of loan performance across different borrower and loan dimensions.
Main analysis areas include:
- Monthly loan trends
- Loan distribution by state
- Loan distribution by loan term
- Loan amount by employment length
- Loan amount by loan purpose
- Loan amount by home ownership
This page helps users explore portfolio patterns by geography, borrower profile, and loan characteristics.
The Details page provides a loan-level view for detailed investigation.
It allows users to review individual loan records, including:
- Loan ID
- Purpose
- Home Ownership
- Grade
- Sub-grade
- Loan Status
- Funded Amount
- Amount Received
- Interest Rate
- Debt-to-Income Ratio
This page supports record-level monitoring and more detailed loan investigation.
| KPI | Description |
|---|---|
| Total Loan Applications | Total number of loan applications |
| Total Funded Amount | Total loan amount funded by the bank |
| Total Amount Received | Total repayment amount received from borrowers |
| Average Interest Rate | Average interest rate across all loans |
| Average DTI | Average debt-to-income ratio |
| Good Loan Percentage | Percentage of loans with healthy repayment status |
| Bad Loan Percentage | Percentage of loans with problematic repayment status |
| MTD Metrics | Month-to-date performance indicators |
| MoM Metrics | Month-over-month performance comparison |
Loans are categorized into two main groups:
Good loans include loans with healthy repayment status, such as:
- Fully Paid
- Current
Bad loans include loans with problematic repayment status, such as:
- Charged Off
This classification helps evaluate loan portfolio quality and identify potential credit risk patterns.
Some key observations from the dashboard include:
- Good loan and bad loan segmentation provides a clear overview of portfolio quality.
- Funded amount, amount received, interest rate, and DTI are important indicators for monitoring loan performance.
- Loan performance varies across borrower characteristics such as employment length, home ownership, loan purpose, and grade.
- Geographic analysis helps identify regional loan distribution patterns.
- Monthly trend analysis supports monitoring of portfolio growth and repayment performance over time.
Through this project, I practiced:
- Building an end-to-end Power BI dashboard
- Creating KPI cards and interactive report pages
- Cleaning and transforming data using Power Query
- Creating DAX measures for business KPIs
- Using slicers and filters to improve dashboard interactivity
- Designing multi-page dashboards for summary and detailed analysis
- Translating raw loan data into business insights
- Presenting finance-related analysis in a structured way
bank-loan-analysis/
│
├── BankLoan.pbix
├── README.md
└── data/
- Download the
BankLoan.pbixfile from this repository. - Open the file using Power BI Desktop.
- Explore the dashboard pages:
- Summary
- Overview
- Details
- Use slicers and filters to analyze different loan segments.
Dashboard screenshots are not included in this version of the repository. The full interactive dashboard can be viewed by downloading and opening the .pbix file in Power BI Desktop.
This project was built as a guided learning project based on a public Power BI tutorial. The purpose of this repository is to demonstrate hands-on practice in Power BI dashboard development, financial KPI analysis, and data storytelling for a Data Analyst portfolio.