Optimization of Enterprise Financial Risk Management and Crisis Early Warning System Supported by AI

Abstract

The stability of the financial market plays a key role in the overall health of the economic system, and optimizing corporate financial risk management and crisis warning systems is of great significance. Traditional financial risk management methods often face challenges when processing large-scale, resulting in a decrease in the accuracy of risk assessment. This study combining gated recurrent units (GRU), temporal convolutional networks (TCN) and attention mechanisms. Leverage diverse data sets to improve the accuracy of financial risk assessments and the effectiveness of crisis warnings. Experimental results show that the algorithm outperforms the baseline model in terms of risk prediction accuracy, efficiency and stability. For example, on the LendingClub loan dataset, FLOPs are reduced by more than 46.8%, inference time is improved by more than 48.5%. This method provides new perspectives and solutions for optimizing corporate financial risk management and crisis warning systems. This will have a positive impact on the stable operation of enterprises and other aspects.