Research on Fraudulent Transaction Detection Technology

With the popularization of digital payment and the rapid development of financial technology, the problem of fraudulent transactions has become increasingly serious, bringing huge economic losses and trust crises to individuals, enterprises and society. This paper systematically discusses the definition, types, characteristics and influence of fraudulent transactions, and deeply analyzes the current mainstream fraudulent transaction detection techniques, including machine learning, generative adversarial network (GAN) and graph neural network (GNN) methods. It is found that although these technologies show high accuracy and robustness in the detection of fraudulent transactions, they still face limitations such as data imbalance, poor model interpretation, and insufficient public data sets. In order to solve these problems, this paper puts forward some improvement measures such as combining multiple models, data enhancement technology, federated learning and model visualization, and looks forward to the future development direction, such as reinforcement learning, blockchain technology and multi-modal data fusion application. This paper aims to provide a systematic reference for researchers and industry practitioners to promote the development of more efficient and intelligent fraud detection technologies, thereby improving financial security and reducing fraud risks.