Bank data protection and fraud identification based on improved adaptive federated learning and WGAN

To enhance the protection of bank data privacy, build a more secure and efficient data privacy protection system, and effectively identify transaction fraud, this study first proposes an improved adaptive data protection architecture by combining federated learning and differential privacy technology. Then, a fraud recognition model is designed based on generative adversarial networks. The experiment outcomes show that the adaptive data protection architecture designed in this study notably raises the convergence performance of the loss function after introducing adaptive gradient pruning and noise differentiation strategies, with a minimum loss value of 0.09 and excellent learning ability. The accuracy of this method can reach 0.996, and its performance advantage is significant under low privacy budget. Meanwhile, after introducing attacks, the accuracy of this method decreases slightly and the probability of privacy leakage is low. The values of mutual information and re-identification risk analysis are the smallest, converging to 0.02 and 0.194, respectively. The class imbalance foundation of the research design validly raises the capability benefits of the dataset, and the fraud recognition model constructed from it has the best capability and the recognition precision. The missed and false alarm rates fluctuate within the range of 0.15-0.35. In summary, the research improves the data privacy protection capabilities of banks, which helps to reduce financial risks and enhance customer trust.