Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP DOI Creative Commons

Bingya Wu,

Zeqing Hu, Zhouyi Gu

и другие.

Data, Год журнала: 2025, Номер 10(1), С. 9 - 9

Опубликована: Янв. 14, 2025

Technology-based small and micro enterprises play a crucial role in national economic social development. Managing their credit risk effectively is key to ensuring healthy growth. This study based on corporate management theory Wu’s three-dimensional theory. It clarifies the concept measurement logic of these enterprises, considering unique development characteristics China. A evaluation system constructed, an innovative method combining machine learning with comprehensive proposed. approach aims assess status technology-based thorough objective manner. The finds that, first, level currently moderate, little variation. Second, financial information remains factor evaluation. Third, ML-AHP (Machine Learning-Analytic Hierarchy Process) combined weighting integrates subjective experience data, providing more rational assessment. findings provide theoretical references practical guidance for early warning, improved financing efficiency.

Язык: Английский

Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP DOI Creative Commons

Bingya Wu,

Zeqing Hu, Zhouyi Gu

и другие.

Data, Год журнала: 2025, Номер 10(1), С. 9 - 9

Опубликована: Янв. 14, 2025

Technology-based small and micro enterprises play a crucial role in national economic social development. Managing their credit risk effectively is key to ensuring healthy growth. This study based on corporate management theory Wu’s three-dimensional theory. It clarifies the concept measurement logic of these enterprises, considering unique development characteristics China. A evaluation system constructed, an innovative method combining machine learning with comprehensive proposed. approach aims assess status technology-based thorough objective manner. The finds that, first, level currently moderate, little variation. Second, financial information remains factor evaluation. Third, ML-AHP (Machine Learning-Analytic Hierarchy Process) combined weighting integrates subjective experience data, providing more rational assessment. findings provide theoretical references practical guidance for early warning, improved financing efficiency.

Язык: Английский

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