Credit card transaction fraud detection based on machine learning

Abstract

The rise of e-commerce payment systems has been swift due to our society's rapid advancement. However, the transparency and vulnerability of the internet have opened new avenues for illicit access, resulting in a significant surge in financial fraud cases, particularly credit card transaction fraud. Detecting and mitigating such incidents has become crucial. One branch of artificial intelligence, machine learning, offers a potent solution. It operates by utilizing various algorithms and models that rely on established patterns and reasoning without explicit instructions. By processing vast amounts of historical data, machine learning models can identify underlying data relationships, allowing them to make accurate predictions based on input data. Therefore, it emerges as a highly effective method for credit card transaction fraud detection. This paper reviews the research methods for credit card fraud, introduces credit card transaction fraud detection data sets, and outlines machine learning algorithms and models related to credit card fraud detection. It also considers the future prospects for machine learning development and possible challenges.