Credit Decision of Small and Micro-sized Businesses Based on Big Data Analysis
- DOI
- 10.2991/978-94-6463-058-9_82How to use a DOI?
- Keywords
- Small And Micro Sized Businesses; Big Data Analysis; Credit Decision; Risk AssessmentModel; Post-Epidemic Era
- Abstract
In this paper, we select big data information such as credit risk, credit policy and transaction bill information of small and micro businesses , screen enterprises that do not meet the evaluation requirements, establish credit indicators, and evaluate the strength of enterprises. Based on the big data analysis, this paper establishes a correlation regression analysis model, evaluates the relationship between credit and transaction information of enterprises, and gives the optimal credit decision scheme model. By collecting the impact of COVID-19 epidemic on different types of enterprises, the small and medium-sized enterprises selected as the research object in this paper are divided into six categories, and their different impact indexes are obtained,Then, 302 enterprises are classified according to their size, and their resistance to sudden factors is established,The possible sudden factors are analyzed, and the corresponding adjustment suggestions for their credit strategies in the post-epidemic era are given.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wenjing Wang PY - 2022 DA - 2022/12/27 TI - Credit Decision of Small and Micro-sized Businesses Based on Big Data Analysis BT - Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022) PB - Atlantis Press SP - 501 EP - 506 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-058-9_82 DO - 10.2991/978-94-6463-058-9_82 ID - Wang2022 ER -