UPI Fraud Detection System

A comprehensive machine learning-based system for detecting fraudulent transactions in UPI (Unified Payments Interface) payment networks. This project combines advanced ML algorithms, real-time monitoring, and interactive visualization to provide a robust fraud detection solution.

Repository

GitHub: https://github.com/Skismail57/UPIFraudDetectionUsingMachineLearning

Features

• Real-time Transaction Monitoring: Analyze transactions as they occur to detect suspicious patterns
• Multi-model Ensemble Detection: Combines multiple ML models for higher accuracy
• Interactive Dashboard: Visualize fraud patterns and system performance metrics
• API Integration: Easy integration with existing payment systems
• Graph Neural Networks: Detect complex fraud patterns and collusion networks
• Explainable AI: Understand why transactions are flagged as fraudulent
• Scalable Architecture: Designed to handle high transaction volumes

Technologies Used

• Machine Learning: scikit-learn, XGBoost, LightGBM
• Deep Learning: PyTorch, Graph Neural Networks
• API Framework: FastAPI
• Frontend: HTML, CSS, JavaScript
• Visualization: Interactive charts and graphs
• Deployment: Docker, Kubernetes support
• Monitoring: Prometheus, Grafana integration

Getting Started

Prerequisites

• Python 3.8+
• pip package manager
• Virtual environment (recommended)

Installation

1. Clone the repository

git clone https://github.com/yourusername/upi-fraud-detection.git
cd upi-fraud-detection

2. Create and activate a virtual environment (optional but recommended)

python -m venv .venv
# On Windows
.venv\Scripts\activate
# On Unix or MacOS
source .venv/bin/activate

3. Install dependencies

pip install -r requirements-fixed.txt

Running the System

Basic Mode

Run the basic fraud detection system:

python quick_start.py

Frontend Dashboard

Start the frontend dashboard:

python frontend/server.py

Advanced Mode

For advanced features and models:

python advanced_quick_start.py

System Architecture

The system consists of several components:

1. Data Ingestion Layer: Processes incoming transaction data
2. Feature Engineering: Extracts and transforms relevant features
3. Model Ensemble: Multiple models working together for detection
4. Decision Engine: Makes the final fraud determination
5. API Layer: Exposes functionality to external systems
6. Dashboard: Visualizes results and system performance

Future Enhancements

• Federated learning for privacy-preserving fraud detection
• Blockchain integration for immutable audit trails
• Advanced anomaly detection with reinforcement learning
• Mobile app for alerts and notifications
• Integration with additional payment platforms

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Structure

The project directory is organised as follows:

├── advanced_quick_start.py     # Advanced system startup
├── quick_start.py              # Basic system startup
├── frontend/                   # Frontend web interface
│   ├── server.py               # Frontend server
│   ├── index.html              # Main HTML page
│   ├── script.js               # Frontend JavaScript
│   └── styles.css              # CSS styles
├── dashboard/                  # React dashboard
│   ├── src/                    # React source code
│   └── public/                 # Public assets
├── models/                     # ML model files
│   ├── gnn/                    # Graph Neural Network models
│   ├── tabular/                # Tabular data models
│   └── sequence/               # Sequence models
├── serving/                    # Model serving components
│   └── models/                 # Model implementations
├── data/                       # Data storage
├── config/                     # Configuration files
├── docs/                       # Documentation
├── tests/                      # Test files
└── infra/                      # Infrastructure code
    └── k8s/                    # Kubernetes configurations

Screenshots

API Documentation

API Endpoints

Dashboard Overview

Real-time Transactions

Fraud Detection Trends

License

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

Author

© 2025 S K Ismail

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