Application of Residual Graph Attention Networks Algorithm in Credit Evaluation for Financial Enterprises

In the context of digital transformation of enterprises, credit evaluation of financial enterprises faces new challenges and opportunities. Digital transformation introduces a large amount of data and advanced analytical tools, providing richer information and methods for credit evaluation. In this paper, we propose a credit evaluation model based on improved quantum genetic algorithm and residual graph attention network (DRQGA-ResGAT), which aims to utilize the complex correlation data and multi-dimensional information among enterprises for enterprise credit evaluation. The credit evaluation model based on DRQGA-ResGAT performs well in dealing with large-scale and high-dimensional data and can significantly improve the accuracy of credit evaluation. The experimental results show that the ResGAT model combined with the improved quantum genetic algorithm performs even better, and the proposed model has a high precision rate in the credit evaluation of financial enterprises, which has a greater application value. Compared with the traditional ResGAT model, the model improves about 17.06% in precision rate.

@article{Zeng2024,
  title     = {Application of Residual Graph Attention Networks Algorithm in Credit Evaluation for Financial Enterprises},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wenxing Zeng},
  doi       = {10.14569/IJACSA.2024.0151261},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151261}
}