In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management.

在全球化金融市场中，商业银行面临不断升级的信贷风险，从而对银行资产安全和金融稳定性提出更高要求。本研究利用先进的神经网络技术，特别是反向传播神经网络，开创了一种新的模型来预测商业银行的信贷风险。通过比较分析，阐明了神经网络模型在预测商业银行信贷风险方面的优越性。实验结果表明，该模型有效提高了信贷风险管理的远见性和准确性。

基于神经网络算法的商业银行信用风险预警模型研究