Digital Economy Risk Prevention and Control System with Generative Adversarial Networks

The current digital economy is developing rapidly, and the prevention and control of digital risks has become a major issue. This article established a model that can predict risks by combining the generation and discrimination of generative adversarial networks, and then tested the performance of the system through experiments. By collecting digital economy transaction records, a dataset was established as training and testing data, and an adversarial generative network model was designed, with generators used to generate forged risk event samples and discriminators used to identify samples. Then, the dataset was used to train the GAN model and optimize the parameters of the generator and discriminator. Finally, by predicting and evaluating the test data, the performance indicators of the system were analyzed. Through experiments, the system's false alarm rate in this article ranged from 0.01 to 0.04. The digital economy risk control system based on generative adversarial networks can accurately identify and capture real risk events while reducing missed and false alarm rates, and prevent false alarms as much as possible.