Enhancing Security in E-commerce and E-payment Systems through the Implementation of Machine Learning for Credit Card Fraud Detection
Keywords:
E-commerce, E-payment systems, Fraud detection, Machine learningAbstract
The expeditious growth of electronic payment and e-commerce systems has brought unparalleled convenience to consumers globally. Yet, this digital transformation has concurrently fueled an increase in financial deception, notably under the guise of credit card fraud. It is paramount to identify and thwart these illicit activities to safeguard the trust and integrity of online transactions. In this paper, an innovative method is proposed that utilized the machine learning method in the realm of credit card fraud detection, with a specific emphasis on fortifying the security of e-commerce and electronic payment systems. In this scheme, we conducted extensive experiments using a dataset obtained from Kaggle in order to evaluate the effectiveness of our proposed technique. Different classifiers namely LR, DT and RF have been used in this proposed scheme. The RF model with oversampling yielded the highest accuracy, recall, precision, and F1 Score, which is 99.97%. Accordingly, it shows that RF classifiers are effective for oversampling. The outcomes of our assessment underscore its capability to notably bolster security within e-commerce and electronic payment systems. These findings emphasize the significance of harnessing machine learning methodologies in the continual effort to combat credit card fraud in the era of digital transactions.
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