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Research on Business Data-driven Risk Prediction Methods Based on Machine Learning
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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.
Keywords
Business data; Machine learning; Risk prediction; Feature engineering; Model validation
References
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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
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