A Review of Random Forest Applications in Financial Risk Assessment

Yongzhi Liu

doi:10.47297/wspdecWSP2515-797307.20250910

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A Review of Random Forest Applications in Financial Risk Assessment

Volume 9, Issue 10

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A Review of Random Forest Applications in Financial Risk Assessment
A Review of Random Forest Applications in Financial Risk Assessment
Computer Science, Guangdong University of Science and Technology, Intelligent Science and Technology, Dongguan, Guangdong, 523668, China
Vol. 9, Issue 10, Pages: 35-40(2025)
Published：30 October 2025
DOI：10.47297/wspdecWSP2515-797307.20250910
Accepted：

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Liu Yongzhi.A Review of Random Forest Applications in Financial Risk Assessment[J].Development Economics of China,2025,09(10):35-40.
DOI：

Liu Yongzhi.A Review of Random Forest Applications in Financial Risk Assessment[J].Development Economics of China,2025,09(10):35-40. DOI： 10.47297/wspdecWSP2515-797307.20250910.

摘要

Abstract

With the surge in complexity and data volume of financial markets

traditional risk assessment methods face limitations when dealing with high-dimensional

nonlinear

and dynamic data. Random Forest

as an ensemble learning algorithm

has demonstrated outstanding performance in numerous fields since its theoretical framework was proposed by Leo Breiman in 2001. By combining multiple decision trees

it significantly enhances prediction accuracy and robustness

leading to its widespread application in financial risk assessment in recent years. This paper systematically reviews the applications of Random Forest in areas such as credit risk

market risk

and operational risk

analyzing its principles

advantages

and performance. Research indicates that Random Forest outperforms traditional models (such as XGBoost and Neural Networks) in terms of prediction accuracy

feature interpretability

and resistance to overfitting

but it also faces challenges related to interpretability and computational efficiency.

关键词

Keywords

references

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