Volume - 6 | Issue - 4 | december 2024
Published
07 February, 2025
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.
KeywordsCredit Card Fraud Detection Machine Learning deep learning Online Banking Security Fraud Prevention