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

In: Liss 2023

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lei Han & Qixin Bo & Guiying Wei & Yingxue Pan, 2024. "Research on SMEs Credit Risk Prediction Based on Decision Tree and Random Forest," Lecture Notes in Operations Research, in: Daqing Gong & Yixuan Ma & Xiaowen Fu & Juliang Zhang & Xiaopu Shang (ed.), Liss 2023, pages 366-378, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-4045-1_29
    DOI: 10.1007/978-981-97-4045-1_29
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