Fraud Detection System

A machine learning-based system for detecting potentially fraudulent transactions in real-time.

Overview

This project implements a fraud detection system using machine learning to identify suspicious transactions. The system analyzes various features of transactions, including:

• Transaction amount and timing
• Customer transaction history
• Terminal transaction history
• Temporal patterns

Demo

Here are examples of how the system evaluates transactions:

Input Interface

The user interface for entering transaction details and historical statistics

Example Results

Legitimate Transaction Example

A normal transaction with low fraud probability and no risk factors

Fraudulent Transaction Example

A suspicious transaction flagged with multiple risk factors showing:

• High transaction amount (>220)
• Unusual amount for the customer
• Unusual amount for the terminal

Features

• Real-time fraud detection
• Web-based interface using Streamlit
• Model training and evaluation
• Transaction testing capabilities
• Feature importance visualization
• Risk factor analysis

Project Structure

fraud_detection/
├── app.py                 # Streamlit web application
├── fraud_detection.py     # Main training and model code
├── test_fraud_detection.py # Transaction testing script
├── convert_to_csv.py      # Data conversion utility
├── requirements.txt       # Python dependencies
└── README.md             # Project documentation

Setup

1. Clone the repository:

git clone https://github.com/yourusername/fraud_detection.git
cd fraud_detection

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Usage

Training the Model

1. Place your transaction data in the data directory
2. Run the training script:

python fraud_detection.py

Testing Transactions

1. Use the test script to check individual transactions:

python test_fraud_detection.py

Web Interface

1. Start the Streamlit app:

streamlit run app.py

2. Open your browser and navigate to http://localhost:8501

Features Used for Detection

• Transaction Amount
• Log-transformed Amount
• Time of Transaction (Hour, Day, Month)
• Customer Statistics:
  • Mean Transaction Amount
  • Standard Deviation
  • Transaction Count
• Terminal Statistics:
  • Mean Transaction Amount
  • Standard Deviation
  • Transaction Count

Model Performance

The system uses a Random Forest classifier with the following characteristics:

• Handles class imbalance
• Provides probability estimates
• Evaluates multiple risk factors
• Visualizes feature importance

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Acknowledgments

• Data preprocessing techniques
• Feature engineering approaches
• Machine learning model implementation

Dataset Description

The dataset contains simulated transaction data with the following columns:

• TRANSACTION_ID: Unique identifier for the transaction
• TX_DATETIME: Date and time of the transaction
• CUSTOMER_ID: Unique identifier for the customer
• TERMINAL_ID: Unique identifier for the merchant terminal
• TX_AMOUNT: Amount of the transaction
• TX_FRAUD: Binary variable (0 for legitimate, 1 for fraudulent)

Fraud Patterns

The system detects fraud based on three main patterns:

1. Transactions with amount > 220
2. Transactions from compromised terminals
3. Unusual spending patterns from compromised customer accounts