Risk Early Warning of Enterprise Financial Management Risk Based on CNN and BiLSTM under Accounting Informatization

Based on accounting informatization, this paper constructs a inancial risk prediction system by applying the CNNs (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory)-Attention model to accurately identify and classify various risk types in enterprise FM ( inancial management), and improve the accuracy and ef iciency of inancial risk prediction.CNN was used to extract local features in inancial data, BiLSTM was used to capture time dependencies, and inally the importance of inancial indicators was weighted and fused through the Attention mechanism.During the training process, the Adam optimizer and cross entropy loss function are used for optimization, and appropriate learning rates and training rounds are set to ensure the stability and performance of the model.The experimental results show that when the epochs is 50, the accuracy of risk classi ication is 98.9% and the loss value is 0.012.In the analysis of each data level, the average response time of the proposed system and the traditional system is 1.80s and 7.17s respectively.The system in this paper shows obvious advantages in response time and prediction accuracy.The response time is greatly shortened, and it can provide effective support in real-time decision-making.This paper model has signi icant application prospects in inancial risk prediction, and can provide enterprises with ef icient and accurate risk warnings, which has important theoretical signi icance and practical value.