@article{4314,
  author = {Xiaojun Li, Xiyan Han},
  title = {Financial Company Risk Prediction in the AI Era},
  journal = {Journal of Information Security Research},
  year = {2024},
  volume = {15},
  number = {4},
  doi = {https://doi.org/10.6025/jisr/2024/15/4/155-162},
  url = {https://www.dline.info/jisr/fulltext/v15n4/jisrv15n4_4.pdf},
  abstract = {Integrating the Internet and financial companies has expanded the market and
avenues for personal loans. While the scale of personal loans has rapidly expanded,
it has also brought higher default rates. This paper constructs a personal loan
default risk prediction model based on an improved LightGBM model to control
default rates and reduce financial company risks. The model’s accuracy in
predicting default risks is enhanced by optimizing model parameters and
supplementing the evaluation system witha particle swarm optimization algorithm.
Experimental results show that compared to four other risk prediction models,
this model performs better in predicting default risks. The introduced indicators
effectively reduce prediction errors, resulting in higher model accuracy and a better
fit to real-world scenarios.},
}