This paper focuses on the application and optimization of LSTM model in
financial risk prediction. The study starts with an overview of the
architecture and algorithm foundation of LSTM, and then details the model
training process and hyperparameter tuning strategy, and adjusts network
parameters through experiments to improve performance. Comparative experiments
show that the optimized LSTM model shows significant advantages in AUC index
compared with random forest, BP neural network and XGBoost, which verifies its
efficiency and practicability in the field of financial risk prediction,
especially its ability to deal with complex time series data, which lays a
solid foundation for the application of the model in the actual production
environment.

该研究侧重于 LSTM 模型在金融风险预测中的应用和优化，实验结果表明，经过优化的 LSTM 模型在 AUC 指数上相比随机森林、BP 神经网络和 XGBoost 都有显著优势，验证了其在金融风险预测领域的有效性和实用性，尤其是处理复杂时间序列数据的能力，为该模型在实际生产环境中的应用奠定了坚实基础。