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

arXiv: https://arxiv.org/abs/2405.10762 · HTML: https://mlsanity.com/paper/2405.10762/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 5 of 5)

- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery](https://mlsanity.com/paper/1810.12813/) (2018)
- [ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs](https://mlsanity.com/paper/2305.10649/) (2023)
- [Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research](https://mlsanity.com/paper/2403.14483/) (2024)
- [Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach](https://mlsanity.com/paper/2010.12662/) (2020)

## Built on by (most cited first, 5 of 5)

- [Application of Natural Language Processing in Financial Risk Detection](https://mlsanity.com/paper/2406.09765/) (2024)
- [Exploiting Diffusion Prior for Out-of-Distribution Detection](https://mlsanity.com/paper/2406.11105/) (2024)
- [Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images](https://mlsanity.com/paper/2408.00636/) (2024)
- [Optimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction](https://mlsanity.com/paper/2410.12642/) (2024)
- [Machine Learning-Based Cloud Computing Compliance Process Automation](https://mlsanity.com/paper/2502.16344/) (2025)

## Abstract (arXiv)

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.