Financial Risk Control of Listed Enterprises Based on Risk Warning Model

Abstract

Accurate early warning of financial risks is beneficial for enterprise management. In this paper, a back-propagation neural network (BPNN) was used to predict financial risks in enterprises, and a genetic algorithm (GA) was used to improve the BPNN. Afterward, a case study was carried out, and a comparison with the support vector machine (SVM) and conventional BPNN models was made. The results indicated a significant correlation between the 14 selected early warning indicators and financial risk. The BPNN model improved by GA converged faster during training. Compared with the SVM and conventional BPNN models, the BPNN model optimized by GA had superior early warning performance. The risk assessment and early warning indicators of Company A were analyzed, and several suggestions were put forward based on the analysis results.