Application of Generative Artificial Intelligence Algorithms in Digital Financial Risk Prediction and Prevention Systems

The digital financial sector faces challenges in predicting risk events and ineffective risk prevention and control. This paper explores the application of generative artificial intelligence (AI) algorithms in digital financial risk prediction and prevention systems. By introducing a generative adversarial network (GAN) to optimize sample distribution, leveraging the Transformer architecture to capture dynamic temporal features, combining it with a graph convolutional network (GCN) to model complex relational topologies, and employing a conditional variational autoencoder (CVAE) to decouple risk factors, an end-to-end intelligent risk prevention and control framework is constructed. This framework achieved an AUC of 0.926 and a recall of 0.792 for risk prediction. Application in the prevention and control system has kept the capital loss rate below 3.2%, increased the detection rate of complex money laundering transactions to 81.0%, and reduced the false alarm rate to 0.9%, providing an effective path for intelligent upgrades in digital financial risk management.