Customized integrated decision model for CBEC enterprise credit evaluation: The fusion of multi-source features and machine learning

The cross-border e-commerce (CBEC) industry plays a crucial role in the transformation of foreign trade and the upgrading of innovative development, driven by information technology and international trade policies. However, the distinctive operational pattern of CBEC enterprises necessitates the customization of the corporate credit evaluation framework to their specific features, which is absent in the existing studies. This paper proposes an integrated decision framework that incorporates multi-source features and machine learning algorithms to achieve customized credit evaluation for CBEC enterprises. These credit features contain reconstructed financial features as well as non-financial features such as innovation inputs and outputs, multiple stakeholder sentiment, and supply chain situation. Moreover, based on the Chinese A-share listed enterprises in the CBEC sector, a data set from 2008 to 2022 is collected for conducting the empirical analysis. The robustness of machine learning–based feature evaluation algorithms and the importance of non-financial features for corporate credit prediction are demonstrated through a comparative analysis of different experimental scenarios. The credit foresight model combined with auto regression and neural network also shows superior performance than different baseline models. At last, credit scoring and rating results from the fusion decision framework are employed for fine-grained comparative analysis between credit features to support customized credit repair strategies and alerts, which facilitates the long-term strategic planning of enterprises.