Credit card fraud has become a significant concern for financial institutions and cardholders worldwide, leading to substantial financial losses and compromised personal information. Traditional rule-based methods for detecting fraud often struggle to keep up with the evolving tactics of fraudsters. In recent years, artificial intelligence (AI) techniques have emerged as promising solutions for credit card fraud detection due to their ability to learn from large datasets and adapt to new patterns.

This research presents an innovative approach to credit card fraud detection using AI. The proposed system leverages machine learning algorithms, specifically deep learning models, to analyze transactional data and identify fraudulent activities in real-time. The system utilizes a comprehensive feature set derived from transaction attributes, including transaction amount, merchant information, time of the transaction, and cardholder details.

To develop an accurate and robust fraud detection system, the study employs a supervised learning framework, training the deep learning models with a labeled dataset that includes both fraudulent and legitimate transactions. The models are optimized using techniques such as cross-validation and hyperparameter tuning to improve their performance. The research also explores various deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to capture complex patterns and dependencies within the transactional data.