Improving Credit Card Fraud Detection through Multi-model Deep Learning: A Comparative Study of ANN and VGG Networks
Keywords:
Artificial Neural Network (ANN), Batch normalization, Binary classification, Class imbalance, Credit card fraud detection, Deep learning, Dropout, F1-score, Feature extraction, Financial security, Fraudulent transactions, Hybrid model, PCA, Precision, Recall, SMOTE, VGG16, VGG19Abstract
The discovery of credit card fraud is still very hard because fraudsters are getting smarter, and transaction data is very unequal between classes. This research shows a clever approach that integrates three advanced models—an Artificial Neural Network (ANN) enhanced with batch normalization and dropout, and the VGG16 and VGG19 architectures—to make detection more accurate and reliable. After a lot of data preprocessing, the system starts up. It uses standard scaling to normalize the data, the Synthetic Minority Over-sampling Technique (SMOTE) to ensure balanced distribution, and Principal Component Analysis (PCA) to redundant dimensions. The cleaned dataset is fed into three separate models—a deep ANN with sequential dense layers (256 → 128 → 64 → 32) using ReLU activation, batch normalization for training stability, and dropout (rate = 0.3) to prevent overfitting, as well as VGG16 and VGG19, which are specially designed to operate on reshaped transaction data and extract complex and nuanced fraud-related features through their deep convolutional layers. An analysis of performance shows that the ANN model does a better job than VGG16 and VGG19, which have accuracy rates of 94% and 95%, respectively. It gets results that are 99% spot on. This study compares convolutional models to ANNs and shows that both are useful for finding scams. The suggested method offers an adaptable and effective way to find credit card fraud in real time.
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