Research on Fraudulent Transaction Detection

With the rapid development of global e-commerce and online payments, the number and complexity of fraudulent transactions have increased significantly, bringing serious economic losses and a crisis of trust to financial institutions and consumers. This paper systematically reviews the characteristics, detection methods and research progress of fraudulent transactions. Firstly, the characteristics of fraudulent transactions are analyzed from the aspects of time, place, transaction mode and technical means. Secondly, the advantages and disadvantages of traditional detection methods (such as rule-based and statistical analysis) and emerging technologies (such as machine learning and deep learning) and their application effects are reviewed. Finally, the limitations of current research in data imbalance, model complexity and multi-type fraud detection are discussed, and future research directions are proposed, including data enhancement techniques, integrated learning frameworks and privacy protection strategies. The research of this paper aims to provide theoretical support and practical guidance for the academic and industrial circles, and promote the further development of the financial security field.