Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)

This work aims to improve the accuracy of financial crisis early warnings for small and medium-sized enterprises (SMEs) to help them uncover hidden dangers during the latent period of a crisis and to respond effectively. First, this work analyses the development characteristics of SMEs, the crises they face, and a financial crisis early warning system. Then, a statistical method is used to determine which indicators are significant to implement the model for an early warning financial crisis to determine the index system. To do this, relevant machine learning (ML) algorithms are introduced to realize a corporate financial crisis management and early warning system (CFCM-EWS) using the stacking fusion method. The CFCM-EWS can mine relationships among the data and analyze nonlinear and difficult-to-explain problems. A comparison of classical financial early warning models (backpropagation neural network (BPNN) and logistic regression models) with the XGBoost model highlights the advantages of the XGBoost model. The model's discrimination results show that its prediction accuracy for special treatment and nonspecial treatment corporates is 85.8 % and 81.9 %, respectively. The logistic regression, XGBoost, and BPNN models are fused using the stacking method. ML provides a more practical prediction method with higher efficiency and accuracy than does traditional econometric models. The final fusion model outperforms the voting and averaging methods in prediction performance. The CFCM-EWS based on intelligent computing discussed here is of great value for SMEs to accurately predict financial crises and adopt timely countermeasures.