Abstract
Default risk prediction presents a significant challenge for micro and small enterprises due to the unavailability of comprehensive information databases. This paper develops a default risk management tool based on user portrait theory, utilizing common and objective indicators of micro and small enterprises, such as basic information about entrepreneurs, enterprises, and loans. The Shapley Additive exPlanations (SHAP) method is employed to analyze the contribution of each indicator to default prediction. Empirical results show that household income and personal income are the two most important variables in general, with higher household income associated with a lower probability of default. However, a higher personal income is associated with a higher probability of default. Moreover, the importance of variables and the direction of their relationship with default prediction vary across samples. These findings provide significant insights for developing an accurate default prediction warning system for financial institution managers and policymakers, using the proposed methodology and technical framework.
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The data that support the findings of this study are available on request from the corresponding author, [Yang Cai, 1558325442@qq.com]，upon reasonable request.
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Acknowledgements
We are grateful to the reviewers and the editor for their helpful comments and suggestions. This work was supported by the National Office for Philosophy and Social Sciences of China under Grant 23BTJ044.
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Zheng, C., Weng, F., Luo, Y. et al. Micro and small enterprises default risk portrait: evidence from explainable machine learning method. J Ambient Intell Human Comput 15, 661–671 (2024). https://doi.org/10.1007/s12652-023-04722-6
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DOI: https://doi.org/10.1007/s12652-023-04722-6