Journal of Engineering, Project, and Production Management, 2026, 16(7), 2026-84
An Enterprise Financial Management Technique Using Decision Tree with ID3 Algorithm
Instructor, School of Digital Intelligence and Trade Management, Lishui Vocational and Technical College, Lishui, 323000, China, E-mail: ranran612025@163.com
Project Management
Received January 20, 2026; revised March 18, 2026; accepted September 5, 2026
Available online October 5, 2026
Abstract: In the context of digitalization, enterprise financial management requires precise risk warning. Therefore, an improved generalized iterative binary decision tree model is proposed that enhances robustness by dynamically discretizing continuous attributes, using the information gain ratio criterion, and handling probabilistic missing values. Based on this, a financial risk decision tree model is constructed, which integrates financial indicator weighting, asymmetric misclassification cost-sensitive learning, and a dynamic feedback mechanism. Experiments on the Polish Companies Bankruptcy dataset showed that the accuracy increased by 3.40% compared to the original model, while reducing model complexity by 63.3%. The financial risk decision tree model achieved a high-risk enterprise detection rate of 89.4% and an F1 score of 85.6%, which are 6.6% to 13.6% higher than those of XGBoost, random forest, and support vector machines, while reducing misclassification costs by 43.2%. Managers can use models to accurately identify high-risk enterprises, optimize capital allocation strategies, and implement interventions in advance, thereby effectively reducing potential financial losses.
Keywords: Decision tree; ID3 algorithm; financial risk management; decision support system; enterprise management analysis.
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Citation: Zhang, C.(2026). An Enterprise Financial Management Technique Using Decision Tree with ID3 Algorithm. Journal of Engineering, Project, and Production Management, 16(7), 2026-84.
