Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP
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 clarifi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Data |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5729/10/1/9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588739067510784 |
|---|---|
| author | Bingya Wu Zhihui Hu Zhouyi Gu Yuxi Zheng Jiayan Lv |
| author_facet | Bingya Wu Zhihui Hu Zhouyi Gu Yuxi Zheng Jiayan Lv |
| author_sort | Bingya Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fd3b53b256da4ea187392b8503411e93 |
| institution | Kabale University |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-fd3b53b256da4ea187392b8503411e932025-01-24T13:28:33ZengMDPI AGData2306-57292025-01-01101910.3390/data10010009Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHPBingya Wu0Zhihui Hu1Zhouyi Gu2Yuxi Zheng3Jiayan Lv4School of Information Technology, Zhejiang Financial College, Hangzhou 310018, ChinaSchool of Economics and Management, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information Technology, Zhejiang Financial College, Hangzhou 310018, ChinaSchool of Information Technology, Zhejiang Financial College, Hangzhou 310018, ChinaLibrary, Huzhou University, Huzhou 313000, ChinaTechnology-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.https://www.mdpi.com/2306-5729/10/1/9technology-based small and micro enterprisescredit evaluation systemmachine learningcomprehensive evaluation |
| spellingShingle | Bingya Wu Zhihui Hu Zhouyi Gu Yuxi Zheng Jiayan Lv Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP Data technology-based small and micro enterprises credit evaluation system machine learning comprehensive evaluation |
| title | Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP |
| title_full | Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP |
| title_fullStr | Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP |
| title_full_unstemmed | Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP |
| title_short | Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP |
| title_sort | credit evaluation of technology based small and micro enterprises an innovative weighting method based on machine learning and ahp |
| topic | technology-based small and micro enterprises credit evaluation system machine learning comprehensive evaluation |
| url | https://www.mdpi.com/2306-5729/10/1/9 |
| work_keys_str_mv | AT bingyawu creditevaluationoftechnologybasedsmallandmicroenterprisesaninnovativeweightingmethodbasedonmachinelearningandahp AT zhihuihu creditevaluationoftechnologybasedsmallandmicroenterprisesaninnovativeweightingmethodbasedonmachinelearningandahp AT zhouyigu creditevaluationoftechnologybasedsmallandmicroenterprisesaninnovativeweightingmethodbasedonmachinelearningandahp AT yuxizheng creditevaluationoftechnologybasedsmallandmicroenterprisesaninnovativeweightingmethodbasedonmachinelearningandahp AT jiayanlv creditevaluationoftechnologybasedsmallandmicroenterprisesaninnovativeweightingmethodbasedonmachinelearningandahp |