Research on Financial Risk Warning Based on Intelligent Algorithms

Financial risk early warning has always been one of the important research topics in corporate management decision-making. Timely discovering the existence of financial risks and resolving or avoiding risks in advance can reduce the probability of enterprises falling into financial crisis and avoid certain investment losses for investors. Aiming at the imbalanced sample characteristics of financial risk early warning scenarios, this paper starts from the perspective of improving sample imbalance and takes the construction of a classification model with good generalization performance as the goal to carry out research on financial risk early warning models. To this end, this study uses the particle swarm algorithm to improve the initial weights and thresholds of the neural network, and then uses the optimal solution obtained by the algorithm as the new weights and thresholds of the network. Finally, the fully trained network is used to predict and simulate the financial risk level of listed companies. The research results show that the prediction accuracy of the financial risk early warning model based on the PSO-BP neural network reaches 86.4%, and through the robustness test of the model, it shows that the financial risk early warning model constructed in this paper is reasonable and has application value; by comparing the prediction results with the early warning models based on BP neural network and GA-BP neural network algorithm, it is found that the PSO-BP neural network early warning model is superior to the other two models and has higher prediction accuracy, which can better predict corporate financial risks.