Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm
This addresses credit risk management for commercial banks, but it is incremental as it applies an existing neural network method to a specific domain without new algorithmic breakthroughs.
This study tackled credit risk prediction in commercial banks by developing a neural network model, specifically using Backpropagation (BP), and found it improved foresight and precision compared to traditional methods like ARMA, ARCH, and Logistic regression, though no concrete numbers were provided.
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.