  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.
