Credit Card Fraud Detection System Using Deep Learning Techniques

Credit card fraud detection remains a significant challenge due to the growing complexity of fraudulent behaviour and the severe class imbalance in transaction data. This study presents a hybrid deep learning approach that combines three advanced models—an Artificial Neural Network (ANN) enhanced with Batch Normalization and Dropout, along with VGG16 and VGG19 architectures—to enhance detection accuracy and reliability. The system begins with extensive data pre-processing, including Standard Scaling for normalization, Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution, and Principal Component Analysis (PCA) for dimensionality reduction.