An end-to-end data analytics and business intelligence solution for monitoring loan performance, assessing borrower risk, and enabling smarter banking decisions.
📸 Dashboard Preview
Overview
Summary: Click here to view the dashboard summary video
🏗️ Project Architecture
This project follows a modern data pipeline integrating ETL, SQL, machine learning, and visualization tools to transform raw loan data into actionable insights:
- 🔄 ETL & Data Ingestion: Collect and prepare raw loan data
- 🧹 Python Data Cleaning: Handle rejections and preprocess records
- 🗃️ Oracle SQL Database: Store cleaned data, build staging tables, analytical views, and joins
- 🧠 Python + Scikit-learn: Build risk prediction models
- 📊 Power BI Dashboard: Deliver insights to business users and stakeholders
✨ Key Features
- 📊 Loan Portfolio Overview: Analyze funded amounts, interest rates, and DTI ratios
- ✅ Loan Performance Segmentation: Classify loans as Fully Paid, Current, or Charged Off
- 🌍 Regional Lending Insights: Visualize lending activity by geography
- 👥 Borrower Demographics: Explore employment length, home ownership, and loan purpose
- 🤖 Predictive Analytics (Future Scope): Risk scoring using ML models
🛠️ Tech Stack
| Category | Tools & Libraries |
|---|---|
| Database | Oracle SQL, SQLAlchemy |
| ETL | Python (Pandas, NumPy), OpenPyXL |
| Visualization | Power BI, Plotly, Matplotlib, Seaborn |
| ML / AI | Scikit-learn (classification, forecasting) |
| Versioning | GitHub |
| Development | Jupyter Notebook |
📂 Dataset
The dataset simulates real-world loan portfolios and includes:
- Borrower demographics: income, home ownership, employment length
- Loan details: funded amount, interest rate, term, DTI ratio, purpose
- Repayment history and loan performance categories
⚙️ Installation & Setup
# 1. Clone the repository
git clone https://github.com/Faizan-26079/us-bank-loan-analytics.git
# 2. Import dataset into Oracle SQL Developer
# 3. Run SQL scripts to create schema, staging tables & views
# 4. Load Power BI and connect to Oracle DB
# 5. (Optional) Run Python scripts for ML & exploratory analysis
🎯 Future Enhancements
- 🔮 ML Models: Predictive risk scoring and borrower classification
- ⏳ Time Series Forecasting: Loan origination and repayment trends
- 🌐 Web Deployment: Streamlit or Voila interactive dashboards
- ☁️ Cloud Integration: Automated pipelines with AWS or Azure
📄 License
Licensed under the MIT License — see the LICENSE file for details.
👨💻 Author
Faizan — Data Analytics & Power BI Professional Focused on SQL, Power BI, Python, and scalable data solutions.
🔥 What’s Next?
What improvements or features would you suggest to make this loan analytics dashboard even more impactful?
Feel free to share your ideas, raise issues, or contribute enhancements.
📧 Email: faizankhanofficial71@gmail.com