Intelligent Enterprise Financial Risk Early Warning Using Attention-Optimized BP Neural Network with Hybrid Evolutionary Parameter Tuning

Early warning of enterprise financial risk has a crucial role in avoiding operational failure and ensuring sustainable development. In the past, statistical methods and easy financial ratio analysis usually cannot identify intricate nonlinear relationships between various financial indicators, and predictions are too late or not accurate enough. Current models employing traditional BP (Backpropagation) neural networks enhance prediction with ability to tackle nonlinearities but are plagued by slow convergence, sensitivity to initial weights, overfitting, and not being able to learn to adaptively emphasize important financial features. To counter these issues, this study introduces an AF-BP-EO model that employs an Attention-based Feature optimized BP neural network for enterprise financial risk early warning. The model incorporates an attention mechanism to learn dynamic weighing of key financial metrics, and a hybrid Genetic Algorithm-Particle Swarm Optimization algorithm for optimizing network parameters toward better convergence and predictive accuracy. Adaptive learning rate training is also included to improve generalization across various enterprise datasets. Comparative experiments on actual financial data show that the designed model is superior to the conventional BP and GA-BP networks with respect to accuracy, F1-score, and ROC-AUC, and can also offer timely and credible early warning signals. This study presents a strong and intelligent approach for enterprise financial risk management, with the possibility of practical application in complex business scenarios.