Abstract:
The demand for precise, real-time fraud detection systems is growing in direct correlation with the proliferation of online purchases, since credit card fraud is still a major problem in the banking industry and poses significant dangers to both customers and businesses.This review paper explores various approaches and methodologies developed to detect fraudulent credit card activities, focusing on their applicability, effectiveness, and adaptability in dynamic transaction environments. The paper categorizes existing research based on learning paradigms and detection strategies, highlighting the importance of pattern recognition, anomaly detection, and behavioral analysis in combating fraud.
Examined are important obstacles include data asymmetry, changing fraudulent behavior, and scarce labeled datasets. The paper also discusses common evaluation metrics and performance indicators used to assess detection models. Additionally, it reflects on the role of data preprocessing, feature selection, and system scalability in enhancing detection accuracy.
The review concludes by identifying gaps in current research and proposing future directions, including the development of more adaptive, interpretable, and privacy-preserving fraud detection systems capable of operating efficiently in real-world, large-scale environments.
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