Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm

TL;DR

This paper develops a neural network-based model to improve early warning and prediction of credit risk in commercial banks, aiming to enhance financial stability and asset security.

Contribution

It introduces a novel BP neural network model for credit risk prediction, surpassing traditional models like ARMA, ARCH, and Logistic regression in accuracy and practicality.

Findings

Neural network model outperforms traditional models in credit risk prediction.

The model demonstrates high predictive accuracy on bank data.

Enhanced early warning capabilities for credit risk management.

Abstract

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.