Chen Zhang

Supply chain finance plays a crucial role as a financing channel for small‐ and medium‐sized enterprises (SMEs). However, issues such as financial problems and credit defaults have led to disruptions in this channel. To address credit risk control in SME financing within the field of supply chain finance, this paper focuses on a sample of 506 equipment manufacturing companies listed on the SME board of the Shenzhen Stock Exchange from 2016 to 2020. Taking into consideration, the overall risks faced by these enterprises, the study establishes seven first‐level indicators and identifies 84 candidate second‐level indicators. Partial correlation and variance analysis are then used for the first round of indicator screening, followed by the use of a BP neural network for the second round of selection. As a result, a system of 26 indicators for supply chain financial risk is constructed. The XGBoost model is employed to evaluate the constructed risk index system, while SVM and random forest models are used as comparison models. Bayesian optimization is utilized for parameter tuning of the three models. Empirical results demonstrate that the BO‐XGBoost model reduces prediction errors in comparison to the control models. Furthermore, statistical tests reveal that the predicted values of the BO‐XGBoost model significantly differ from those of the other control models. Compared to other individual models, the BO‐XGBoost model exhibits increased accuracy in credit risk prediction and a significant discriminative effect. These findings highlight the effectiveness of constructing an efficient risk indicator system and utilizing Bayesian optimization for parameter tuning in XGBoost to better differentiate between risky and normal enterprises, thereby minimizing default losses. The research results underscore the advantages of employing Bayesian optimization in XGBoost, which can be applied in credit default prediction for SMEs and serves as a valuable tool in financial risk management and control.

Financial risk, as one of the most influential and destructive risks in business, will make enterprises unable to escape the fate of bankruptcy if not warned and prevented in time. In the paper, we conducted research on the financial risk early warning of listed companies. A total of 250 companies were randomly selected from the Chinese A-share market from 2019 to 2021. By building the 26 financial indicators of listed companies and constructing the PCA-BP neural network, we compared the financial risk early warning effects among PCA-BPNN, SVM, and Logistic. It is found that the financial data processed by PCA can better adapt to the financial risk early warning model. The PCA-BPNN model improved the prediction accuracy of the financial risk early warning, which has strong generalization ability for the prediction of financial risk. Research findings have certain reference significance for precise judgment on the financial risk of companies.