Research on corporate finance credit risk method based on LSTM

As the financial and technology fields are continuously developed in the modern days, FinTech is also quickly developed as an emerging field. Under this background, more and more issues have occurred, in particular, the increased complexity of corporate credit risk management for credit institutions. The traditional methods of risk management cannot satisfy the need of the modern financial industry to control risks efficiently and accurately, for instance, through the use of static blacklists and whitelists. Therefore, the utilization of Large-Scale Artificial Intelligence in credit risk management has become a new trend. The goal of our research is to discover a new appropriate predict approach, which firstly reviews the limitations of the available risk management methods, which are unable to cope with the quickly growing market demand for online financial services and are inadequate in assessing the comprehensive risk of enterprises. To solve these problems, the new Long Short-Term Memory Network is proposed to adopt for credit risk management. The method helps to conduct a comprehensive and accurate analysis on multi-dimensional corporate data through deep learning to assess enterprise risk from business and development aspects. Besides, this research is empirical that could be successfully simulated into data in loaning scenes, proves the new method could handle the risk events and improve the accurate of risk control, raises the facts that bankers are highly efficient in credit approval process. The data of the experiment reveals that the application of the LSTM model in predicting and managing asset financial credit risks still has some apparent advantages, and the practicability is very high. The related research outcomes provide a new high-load risk management tool for the FinTech field, which is of great significance for promoting the innovation and development of the financial industry.