Financial Risk Prediction and Management using Machine Learning and Natural Language Processing

With the continuous development and changes in the global financial markets, financial risk management has become increasingly important for the stable operation of enterprises. Traditional financial risk management methods, primarily relying on financial statement analysis and historical data statistics, show clear limitations when dealing with large-scale unstructured data. The rapid development of machine learning and Natural Language Processing (NLP) technologies in recent years offers new perspectives and methods for financial risk prediction and management. This paper explores and conducts empirical analysis financial risk management using these advanced technologies, with a particular focus on the application of NLP in measuring financial risk tendencies, and the financial risk prediction and management based on a Deep neural network - Factorization Machine (DeepFM) model. Through in-depth analysis and research, this paper proposes a new financial risk management model that combines NLP and deep learning technologies, aimed at improving the accuracy and efficiency of financial risk prediction. This study not only broadens the theoretical horizons of financial risk management but also provides effective technical support and decision-making references for practical operations.