An Intelligent Deep Learning System for Accurate Credit Card Fraud Detection

Online transactions and digital payments have become increasingly common, but this growth
has also led to a rise in credit card fraud, creating serious challenges for both banks and their
customers. Traditional fraud detection methods, such as rule-based systems or standard
machine learning models, often struggle to keep up with the constantly changing tactics used
by fraudsters. Another major issue is that transaction datasets are highly imbalanced—
legitimate transactions far outnumber fraudulent ones—which can reduce accuracy and
increase false alarms. To tackle these problems, this study proposes a deep learning framework
that can detect fraudulent credit card transactions quickly and accurately. The system uses
advanced neural networks to automatically learn complex patterns in transaction data, reducing
the need for manual feature engineering. We also apply data preprocessing and feature
selection steps to improve performance. The model was tested on a well-known benchmark
dataset and compared with traditional methods. Results show a clear improvement in metrics
like accuracy, precision, recall, and F1-score, achieving over 96% detection accuracy while
remaining computationally efficient. This study demonstrates that deep learning-based systems
can provide practical, adaptive, and secure solutions for detecting credit card fraud in today’s
digital payment ecosystem.