ArticleOpen Access http://dx.doi.org/10.26855/acc.2025.10.011
Research on Business Data-driven Risk Prediction Methods Based on Machine Learning
Jiahe Sun
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
*Corresponding author: Jiahe Sun
Published: October 28, 2025
Abstract
A study was conducted on business data-driven risk prediction methods based on machine learning. The objective was to explore how large-scale business data can be leveraged to construct risk prediction models in order to improve risk identification and control capabilities. The methodology combined multi-source business data feature analysis, machine learning model construction, and performance evaluation, with particular focus on critical steps such as data preprocessing, feature engineering, model training, and validation. The results indicated that approaches such as logistic regression, decision trees, support vector machines, and neural networks effectively enhanced the accuracy and stability of risk prediction across different business scenarios, with ensemble learning models demonstrating superior overall performance. The conclusion emphasized that machine learning methods can significantly strengthen risk prediction driven by business data and hold practical value for risk management in fields such as finance and supply chain operations.
Keyword
Business data; Machine learning; Risk prediction; Feature engineering; Model validation
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Copyright
© 2025 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not modified or adapted.
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How to cite this paper
Research on Business Data-driven Risk Prediction Methods Based on Machine Learning
How to cite this paper: Jiahe Sun. (2025) Research on Business Data-driven Risk Prediction Methods Based on Machine Learning. Advances in Computer and Communication, 6(4), 218-223.
DOI: http://dx.doi.org/10.26855/acc.2025.10.011