Corporate Bankruptcy Prediction in the U.S. Using Random Forest and XGBoost Algorithms

DOI

10.2991/978-94-6239-648-7_13How to use a DOI?

Keywords

Bankruptcy Prediction; Random Forest; eXtreme Gradient Boosting; Machine Learning

Abstract

Corporate bankruptcy prediction is crucial to investors, financial institutions and regulators, because it supports early risk warning, enhances capital allocation efficiency, and contributes to both financial market resilience and enterprise sustainability. Traditional statistical methods have limited performance in complex financial data. With the increasing uncertainty of the global economic environment and the growing complexity of enterprise operations, more robust predictive models are urgently needed. Machine learning has demonstrated significant advantages in capturing nonlinear relationships and handling high-dimensional financial indicators. This paper builds a high-precision bankruptcy prediction model based on Random Forest and eXtreme Gradient Boosting (XGBoost) algorithm to improve the prediction performance. The research uses open enterprise financial data, selects key indicators through feature engineering, and compares the performance of two integrated learning algorithms. The experimental results show that XGBoost is better than random forest in accuracy and Area Under Curve(AUC) value, and the feature importance analysis reveals the key factors affecting bankruptcy risk. The model in this paper provides a reliable tool for enterprise risk early warning, and provides an optimization direction for subsequent research. It has important practical and theoretical value.

Copyright

© 2026 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.

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TY  - CONF
AU  - Zihao Lan
PY  - 2026
DA  - 2026/04/24
TI  - Corporate Bankruptcy Prediction in the U.S. Using Random Forest and XGBoost Algorithms
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 107
EP  - 117
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_13
DO  - 10.2991/978-94-6239-648-7_13
ID  - Lan2026
ER  -