Detecting Credit Card Fraud Using 1D Convolutional Neural Network: An Efficient Approach for Enhanced Security

The COVID-19 pandemic promotes the increasing tendency of internet buying even more. Credit cards are frequently utilized in Internet shopping. Credit card fraud detection is becoming more pressing due to the growing damages caused by fraudulent activity, necessitating sophisticated detection approaches. This study uses the one-dimensional Convolutional Neural Network (CNN) to identify fraudulent credit card transactions. Our approach requires careful preparation of transactional data and the development of a specific 1D-CNN architecture designed to identify complex temporal patterns that indicate fraudulent activity. By conducting rigorous experiments on two real-world datasets, our technique significantly improves accuracy for detecting fraud, obtaining 99.77% and 98.89%, respectively. Furthermore, the model demonstrates interpretability and computational efficiency, making it well-suited for easy integration into fraud detection systems. This study presents an adaptable and robust approach utilizing advanced deep learning methods, particularly 1D-CNN, to enhance the security of credit card transactions, effectively solving a critical issue in financial protection.