FraudDetect AI 🛡️

An intelligent fraud detection system for financial transactions using Artificial Intelligence and Machine Learning.

📋 Description

FraudDetect AI is an advanced system that uses Machine Learning algorithms (Random Forest) to analyze financial transactions and detect fraudulent activities with high accuracy. The system provides a comprehensive dashboard with real-time analytics and supports multiple languages.

✨ Features

• 🤖 Intelligent Fraud Detection using Random Forest ML Model
• 📊 Interactive Dashboard with live charts and analytics
• 📁 CSV File Upload & Analysis with automatic processing
• 🌍 Bilingual Support (Arabic & English) with RTL/LTR
• 🌓 Dual Theme Mode (Light & Dark)
• 🏆 Achievements & Points System for user engagement
• 📱 Responsive Design works on all devices
• 📈 Real-time Statistics and threat visualization
• 🔍 Transaction History with detailed analysis

🛠️ Technologies Used

Backend

Frontend

Libraries & Tools

• Flask 3.0.3 - Web framework
• Werkzeug 3.0.1 - WSGI utility library
• Pandas 2.2.2 - Data manipulation and analysis
• NumPy 1.26.4 - Numerical computing
• Scikit-learn 1.5.0 - Machine Learning algorithms
• Chart.js - Interactive charts
• Font Awesome - Icon library

🚀 Installation & Setup

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Git (optional)

Installation Steps as a local

1. Clone the repository:

git clone https://github.com/labeebnaji/WebApp-with-AI.git
cd frauddetect-ai/Frauddetect-ai-WebApp

2. Create a virtual environment (recommended):

python -m venv venv

# Windows
venv\Scripts\activate

# Linux/Mac
source venv/bin/activate

3. Install required packages:

pip install -r requirements.txt

4. Set up environment variables:

# Copy the example file
cp .env.example .env

# Edit .env with your settings

5. Initialize the database:

python -c "from app import app, init_db; init_db()"

6. Run the application:

python app.py

7. Open your browser:

http://127.0.0.1:5000

📁 Project Structure

frauddetect-ai/
├── app.py                      # Main application file
├── schema.sql                  # Database schema
├── requirements.txt            # Python dependencies
├── random_forest_model.pkl     # ML model (trained)
├── .env.example               # Environment variables template
├── .gitignore                 # Git ignore rules
├── LICENSE                    # MIT License
├── README.md                  # Project documentation
├── static/                    # Static files
│   ├── css/
│   │   └── style.css         # Main stylesheet
│   └── js/
│       └── main.js           # JavaScript functionality
├── templates/                 # HTML templates
│   ├── base.html             # Base template
│   ├── index.html            # Dashboard page
│   ├── upload.html           # File upload page
│   ├── alerts.html           # Alerts & analytics page
│   ├── profile.html          # User profile page
│   ├── achievements.html     # Achievements page
│   └── errors/               # Error pages
│       ├── 404.html
│       └── 500.html
└── translations/              # Language files
    ├── ar.json               # Arabic translations
    └── en.json               # English translations

📊 Usage

1. Upload CSV File

• Navigate to "Upload Data" page
• Upload a CSV file containing transactions
• Click "Run Model" to analyze

2. View Results

• Transactions are automatically analyzed
• Fraudulent transactions appear in red
• Safe transactions appear in green
• View detailed statistics on the dashboard

3. Monitor Statistics

• Dashboard displays comprehensive statistics
• Interactive charts for threat visualization
• Overall security score indicator

📝 CSV File Format

The CSV file must contain the following columns:

cc_num,category,amt,zip,lat,long,city_pop,merch_lat,merch_long,trans_day,trans_month,trans_year,trans_hour,trans_minute

Example:

cc_num,category,amt,zip,lat,long,city_pop,merch_lat,merch_long,trans_day,trans_month,trans_year,trans_hour,trans_minute
4532123456789012,grocery,45.50,12345,40.7128,-74.0060,8000000,40.7580,-73.9855,15,1,2025,14,30
6011987654321098,gas_transport,75.20,54321,34.0522,-118.2437,4000000,34.0689,-118.4452,16,1,2025,9,15

Column Descriptions:

• cc_num: Credit card number
• category: Transaction category (grocery, gas_transport, etc.)
• amt: Transaction amount
• zip: ZIP code
• lat, long: Customer location coordinates
• city_pop: City population
• merch_lat, merch_long: Merchant location coordinates
• trans_day, trans_month, trans_year: Transaction date
• trans_hour, trans_minute: Transaction time

🎯 Features Overview

Dashboard

• Real-time threat detection statistics
• Monthly comparison charts
• Security score indicator
• Interactive data visualization

Upload & Analysis

• Drag & drop file upload
• Automatic CSV validation
• Batch transaction processing
• Instant fraud detection results

Alerts & Analytics

• Comprehensive transaction history
• Fraud/Safe transaction filtering
• Detailed transaction information
• Export capabilities (coming soon)

User Profile

• Personal information management
• Social media links
• Activity tracking
• Achievement progress

Achievements System

• Threat Finder: Discover your first security threat
• Security Expert: Discover 50+ security threats
• Cybersecurity Master: Master all security modules

⚙️ Configuration

Environment Variables (.env)

FLASK_APP=app.py
FLASK_ENV=development
SECRET_KEY=your-secret-key-here
DATABASE_URL=frauddetect.db
DEBUG=True
HOST=0.0.0.0
PORT=5000
MAX_CONTENT_LENGTH=16777216

🔒 Security Notes

⚠️ Important: This system is for educational and development purposes. Before using in production:

1. ✅ Enable SECRET_KEY in app.py
2. ✅ Implement user authentication system
3. ✅ Encrypt credit card numbers
4. ✅ Add CSRF Protection
5. ✅ Use HTTPS
6. ✅ Add Rate Limiting
7. ✅ Implement proper logging
8. ✅ Use production-grade database (PostgreSQL/MySQL)
9. ✅ Add input validation and sanitization
10. ✅ Implement backup mechanisms

🚀 Deployment

For Production:

1. Use a production WSGI server:

pip install gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 app:app

2. Set environment to production:

FLASK_ENV=production
DEBUG=False

3. Use a reverse proxy (Nginx/Apache)

4. Enable HTTPS with SSL certificate

5. Use a production database

🧪 Testing

# Run tests (when implemented)
python -m pytest tests/

# Check code coverage
python -m pytest --cov=app tests/

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

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

👥 Authors

• Labeeb Al Baqeri - Initial work

🙏 Acknowledgments

• Flask documentation and community
• Scikit-learn for ML algorithms
• Chart.js for beautiful visualizations
• Font Awesome for icons
• All contributors and supporters

📧 Contact

For questions or suggestions, please open an issue on GitHub.

📸 Screenshots

Dashboard

Upload Page

Results

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Made with ❤️ using Flask & Python

⭐ Star this repo if you find it useful!