  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.
