Enhancing Enterprise Financial Risk Prediction Using LSTM Neural Network

While the use of Long Short-Term Memory (LSTM) neural networks, combined with amino-based modeling, includes some drawbacks related to long-term financial risk predicting, it provides a holistic and flexible framework compared to some traditional predicting models. This process incorporates deep learning, which identify some of the more difficult nonlinear relationships in financial data – a model that recognizes those relationships unlike the traditional linear models (e.g., ARIMA). Amino structure provides flexibility and the temporal capacity of LSTM methodology provides better features, which will enhance advanced anomaly detection and prediction accuracy. This process would improve financial management, and encourage appropriate decision-making regarding investment decisions, risk-exploration, and financial planning.