https://github.com/amitdev3/loan-default-data-analysis
This Power BI project analyzes loan application data to uncover patterns contributing to loan defaults. The project uses SQL for backend querying and Power BI for data modeling and interactive visualizations.
https://github.com/amitdev3/loan-default-data-analysis
dax excel powerbi sql
Last synced: 4 months ago
JSON representation
This Power BI project analyzes loan application data to uncover patterns contributing to loan defaults. The project uses SQL for backend querying and Power BI for data modeling and interactive visualizations.
- Host: GitHub
- URL: https://github.com/amitdev3/loan-default-data-analysis
- Owner: amitdev3
- Created: 2025-05-17T19:21:33.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-19T08:17:50.000Z (about 1 year ago)
- Last Synced: 2025-08-03T19:25:26.019Z (11 months ago)
- Topics: dax, excel, powerbi, sql
- Homepage:
- Size: 136 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## 🔍 Loan Default Prediction & Analysis Dashboard (Power BI + SQL)
This project analyzes a loan dataset to identify patterns and risk factors associated with loan defaults. The goal is to clean the data using SQL in SQL Server and build a Power BI dashboard that provides actionable insights for financial institutions to make better lending decisions and for clear insights.
[Click here to view the project](https://app.powerbi.com/links/lGDMFxh7QI?ctid=a3a31cf5-d129-4352-ab9d-a803b01de947&pbi_source=linkShare&bookmarkGuid=caca3de6-44b4-46b9-ac44-0ccf0f650c57)
## 📊 Dataset Features:
### The dataset includes the following features:
- **Loan Details:** `LoanID`, `LoanAmount`, `InterestRate`, `LoanTerm`, `LoanPurpose`, `Loan Date`
- **Borrower Profile:** `Age`, `Income`, `CreditScore`, `MonthsEmployed`, `NumCreditLines`, `Education`, `EmploymentType`, `MaritalStatus`, `HasMortgage`, `HasDependents`, `HasCoSigner`
- **Target Variable:** `Default` (indicates whether a loan was defaulted)
- **Financial Metrics:** DTIRatio (Debt-to-Income Ratio)
- **Target Variable:** Default (indicates whether a loan was defaulted)
## 🧠 Key Objectives:
- Analyze demographic and financial factors that influence loan default rates
- Explore the relationship between credit score, DTI ratio, and interest rates
- Identify high-risk borrower segments
- Visualize trends over time using the `YOY Amount Change by year`, `YTD Loan Amount by Marital Status`,`Loan by Education type`
## 🛠 Tools Used:
- **Power BI** – for interactive dashboards, slicers, conditional formatting, and bookmarks
- **SQL** – for data transformation and querying
- **Excel** – for initial data preprocessing and data validation (cross checking the counts)
## 💡 Features of the Power BI Dashboard:
- Loan default distribution by age group, income level, and loan purpose
- Trends of loan disbursement and defaults over time
- Conditional formatting to highlight high-risk borrowers
- Filters by employment type, education level, and marital status
## ☁️ Deployment & Automation:
- ✅ Published to Power BI Service for real-time sharing and collaboration
- 🔄 Scheduled Refresh set up to keep data updated on a daily basis
- 🧠 Incremental Refresh enabled to optimize large dataset performance
- 🔐 Row-Level Security (RLS) configured to control access based on user role
This is how first slide of dashboard looks like. 