Corporation Financial Risk Prevention and Control using Long Short-Term Memory Networks

As financial management markets have evolved, the dangers of corporation financial crises and fraud have grown rapidly. Traditional means of preventing corporation financial risk are often unable to keep pace with the complexity and volatility of today’s corporation financial systems. This research addresses ethical issues, budgeting management, privacy concerns, acceptance risks, and the requirement for legislative and regulatory changes to the advancement of AI technology, despite the fact that it offers many advantages, the enterprise corporation financial risk prevention and control based on AI technologies, especially LSTM networks. Long short-term memory (LSTM) is a strong deep learning model that is great for time-series prediction and anomaly detection — suitable for corporation financial risk assessment. This allows the LSTM model to effectively predicts potential risks, detects irregular activities, and improves decision-making processes by analyzing historical corporation financial data, transaction patterns, and market trends. The experimental results show that this method based on AI has a better performance, with an accuracy score of 97.63%, precision of 96.62%, recall of 95.32%, and F1 score of 97.23%, the experimental measure value demonstrates that this solution works effectively. It focuses on improving system, integrating control-based methods for more accurate assessments.