SafeGuard AI Banking Transaction Fraud Detection

SafeGuard AI is a machine learning application designed to detect fraudulent banking transactions. It uses advanced algorithms to identify suspicious activities, generate detailed reports, and help financial institutions prevent fraud.

Table des Matières

• Features
• Prerequisites
• Installation
• Configuration
• Usage
• Project Structure
• Contribution
• License
• Contact

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Features

• Fraud Detection Pipeline : Detects fraudulent transactions using machine learning models like Random Forest.
• Database Support : Connects to PostgreSQL or MySQL databases to retrieve transaction data.
• Dynamic Field Management : Configurable fields to adapt to different database schemas.
• Report Generation : Generates fraud detection reports in CSV or PDF format.
• Modular Design : Clear code structure with separate modules for data loading, model training, and fraud detection services.

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Prerequisites

• Python 3.7 or higher
• pip (Python package manager)
• Git (to clone the repository)
• PostgreSQL ou MySQL database with transaction data
• Virtual Environment (recommended)

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Installation

1. Clone the Repository

git clone https://github.com/malekbig/SafeGuard_AI_Banking_Transaction.git
cd SafeGuard_AI_Banking_Transaction

2. Create a Virtual Environment

It is recommended to use a virtual environment to manage dependencies.

python3 -m venv safeguard-env

3. Activate the Virtual Environment

On macOS/Linux:

source safeguard-env/bin/activate

On Windows:

safeguard-env\Scripts\activate

4. Install Dependencies

pip install -r requirements.txt

Configuration

Before running the application, you need to configure the database connection and other settings.

1. Créer un Fichier de Configuration

Create a configuration file named safeguard_ai_config.yml in the config/ directory.

mkdir config
touch config/safeguard_ai_config.yml

2. Add Configuration Details

Fill in safeguard_ai_config.yml with your specific settings:

database:
  type: postgresql  # or 'mysql'
  host: your-database-host
  port: 5432        # or 3306 for MySQL
  name: your-database-name
  user: your-username
  password: your-password
  schema: your-database-schema

table:
  name: transactions  # Name of the table containing transaction data

fields:
  transaction_id: transaction_id
  user_id: user_id
  amount: amount
  merchant: merchant
  location: location
  transaction_time: transaction_time
  user_frequency: user_frequency
  average_user_amount: average_user_amount
  last_transaction_location: last_transaction_location
  user_country: user_country
  transaction_country: transaction_country
  fraudulent: fraudulent

fraud_detection:
  report_format: csv      # 'csv' or 'pdf'
  report_output_path: reports/  # Directory where reports will be saved

Note: Ensure you replace the placeholders with your actual database and configuration information.

3. Prepare the Database

• Ensure your database is running and accessible.
• Create the required tables using the provided SQL scripts (if applicable).
• Insert sample data into your transactions table for testing.

Usage

1. Run the Application

With the virtual environment activated and the configuration in place, you can run the main script:

python main.py

2. Results

The application will connect to your database, train the fraud detection model, and generate a report. Reports will be saved in the directory specified in report_output_path (e.g., reports/).

3. Logs

The console output will display the progress, including detected fraudulent transactions.

Check the generated report for detailed information.

Project Structure

SafeGuard_AI_Banking_Transaction/
│
├── config/
│   └── safeguard_ai_config.yml          # Configuration file
│
├── data/
│   ├── __init__.py
│   ├── DataLoader.py                    # Loads data from the database
│   └── DatabaseConnector.py             # Manages database connections
│
├── models/
│   ├── __init__.py
│   └── ModelTrainer.py                  # Trains and evaluates ML models
│
├── services/
│   ├── __init__.py
│   └── SafeGuardTransactionService.py   # Detects fraud and generates reports
│
├── reports/                             # Generated reports (added to .gitignore)
│
├── main.py                              # Main execution script
├── requirements.txt                     # Python dependencies
└── README.md                            # Project documentation

Contribution

Contributions are welcome! Please follow these steps:

1. Fork the repository to your own GitHub account.

2. Clone the project to your machine:

git clone https://github.com/votre-utilisateur/SafeGuard_AI_Banking_Transaction.git

3. Create a branch for your feature or bug fix:

git checkout -b feature/your-feature-name

4. Commit your changes with clear messages:

git commit -am "Add a new feature"

5. Push to your branch:

git push origin feature/your-feature-name

6. Create a Pull Request on the original repository.

Licence

This project is under the MIT License.

Contact

For any questions or suggestions, please open an issue or contact the project maintainer.

Note: Disclaimer: This project is intended for educational purposes. Always ensure compliance with all applicable laws and regulations when handling financial data.