Abstract
The rapid growth of e-commerce and online banking has resulted in a substantial rise in credit card fraud incidents. Consequently, machine learning and advanced deep learning techniques have emerged as critical solutions. This study integrates the findings of a few researchers, examining diverse methodologies, including Naïve Bayes, K-Nearest Neighbor (KNN), Logistic Regression, CNN, RNN, and ensemble learning. A comparative performance analysis, emphasizing the challenges posed by imbalanced datasets, demonstrates the superior performance of hybrid models, in enhancing the accuracy of detecting the fraudulent in credit card transactions.
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