A professional-grade AI-powered credit risk assessment platform that predicts loan default probability using advanced machine learning, helping financial institutions make data-driven lending decisions.

• Real-time Risk Assessment: Instant loan default probability calculation
• OCR Document Processing: Extract data from loan applications and financial documents
• Batch Processing: Analyze hundreds of applications simultaneously
• Interactive Risk Modeling: Real-time parameter adjustment and scenario testing
• Comprehensive Analytics: Market trends and risk factor analysis

• XGBoost ML Model: 75%+ accuracy with 150K+ training samples
• Enterprise Architecture: Scalable, maintainable codebase with TypeScript
• Professional UI/UX: Banking-grade interface with responsive design
• API-First Design: RESTful APIs with comprehensive error handling
• Type Safety: Full TypeScript implementation with strict typing

src/
├── types/           # TypeScript type definitions
├── services/        # Business logic and external API integrations
├── utils/           # Utility functions and validation
├── constants/       # Application constants and configuration
├── hooks/           # Custom React hooks
└── lib/             # Shared libraries and utilities

components/          # React components
├── ui/             # Reusable UI components
├── forms/          # Form components
└── charts/         # Data visualization components

app/                # Next.js App Router
├── api/            # API routes
├── (dashboard)/    # Dashboard pages
└── globals.css     # Global styles

Frontend

• Next.js 15 (App Router)
• TypeScript
• Tailwind CSS
• Recharts
• Radix UI

Backend & ML

• Python 3.8+
• XGBoost
• Pandas & NumPy
• Scikit-learn
• Tesseract.js (OCR)

Infrastructure

• Node.js 18+
• PostgreSQL (optional)
• Docker support
• Vercel deployment ready

• Node.js 18+
• Python 3.8+
• Git

1. Clone the repository

   git clone https://github.com/yourusername/loan-default.git
   cd loan-default

2. Install dependencies

   npm install
   pip install -r requirements.txt

3. Environment setup

   cp .env.example .env.local
   # Edit .env.local with your configuration

4. Train the ML model

   python scripts/01_data_processing.py
   python scripts/02_exploratory_analysis.py
   python scripts/03_train_model.py

5. Start development server

   npm run dev

6. Open application Navigate to http://localhost:3000

This project is licensed under the MIT License - see the LICENSE file for details.