Research on SMEs Credit Risk Prediction Based on Decision Tree and Random Forest

Lei Han (), Qixin Bo (), Guiying Wei () and Yingxue Pan ()
Additional contact information
Lei Han: University of Science and Technology, Beijing, China
Qixin Bo: University of Science and Technology, Beijing, China
Guiying Wei: University of Science and Technology, Beijing, China
Yingxue Pan: University of Science and Technology, Beijing, China

A chapter in LISS 2023, 2024, pp 366-378 from Springer

Abstract: Abstract SMEs (small and medium enterprises) are more prone to default due to the problem of information asymmetry with banks and a lack of suitable collateral. Banks face both opportunities and challenges due to the high demand for loans from SMEs, and identifying credit risk has become their primary concern. Restructured sentence for clarity and concision. This paper aims to predict the credit loan status of SMEs in Chinese banks by utilizing an open data set provided by the Digital China Innovation Competition. Decision tree and random forest models are used to construct a classification model, which is then analyzed along with its important attributes. Results indicate that both pruning decision tree and random forest models are effective in identifying credit risks for SMEs.

Keywords: SMEs; Credit risk; decision tree; random forest (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-97-4045-1_29

Ordering information: This item can be ordered from
http://www.springer.com/9789819740451

DOI: 10.1007/978-981-97-4045-1_29

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().