Guarding transactions with AI-powered credit card fraud detection and prevention
FraudGuardAI is an advanced credit card fraud detection system leveraging AI and machine learning techniques to identify and prevent fraudulent transactions in real-time. By analyzing transaction patterns and user behaviors, it aims to enhance security measures for financial institutions and e-commerce platforms.
- Real-Time Fraud Detection: Monitors transactions in real-time to identify potential fraud.
- AI & ML Integration: Utilizes machine learning models to improve detection accuracy over time.
- Scalable Architecture: Designed to handle high volumes of transactions efficiently.
- User-Friendly Interface: Provides an intuitive interface for monitoring and managing alerts.
- Python: Core programming language for model development and data processing.
- Scikit-learn: Machine learning library for building and evaluating models.
- Pandas: Data manipulation and analysis.
- NumPy: Numerical computing.
- Gradio: Framework for creating user interfaces for machine learning models.
To set up the project locally:
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Clone the repository:
git clone https://github.com/lakshug23/FraudGuardAI.git cd FraudGuardAI -
Install dependencies:
pip install -r requirements.txt
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Run the application:
python gradio_app.py
This will start a local server and open the Gradio interface in your browser.
Once the application is running, you can input transaction details into the Gradio interface to receive real-time fraud risk assessments. The model will analyze the inputs and provide a prediction indicating the likelihood of fraud.
- OpenAI: For providing access to advanced AI models.
- Gradio: For offering an easy-to-use interface framework.
- Scikit-learn: For its robust machine learning tools.