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

Author

Listed:
  • Bingya Wu

    (School of Information Technology, Zhejiang Financial College, Hangzhou 310018, China)

  • Zhihui Hu

    (School of Economics and Management, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Zhouyi Gu

    (School of Information Technology, Zhejiang Financial College, Hangzhou 310018, China)

  • Yuxi Zheng

    (School of Information Technology, Zhejiang Financial College, Hangzhou 310018, China)

  • Jiayan Lv

    (Library, Huzhou University, Huzhou 313000, China)

Abstract

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

Suggested Citation

  • Bingya Wu & Zhihui Hu & Zhouyi Gu & Yuxi Zheng & Jiayan Lv, 2025. "Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP," Data, MDPI, vol. 10(1), pages 1-21, January.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:1:p:9-:d:1566429
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    References listed on IDEAS

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    Cited by:

    1. Yu, Zemin & Shi, Enyi, 2025. "Green finance and corporate credit ratings - Exploring the potential of the sharing economy," Finance Research Letters, Elsevier, vol. 86(PG).

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