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<p>As the complexity of corporate financial management increases, conventional financial risk identification methods suffer from limitations in real-time responsiveness, accuracy, and automation, often relying on static data and manual analysis. This study addresses these shortcomings by proposing a real-time financial risk identification and prevention system based on the random forest (RF) algorithm. Unlike prior models, which typically focus on static historical data and lack integration with live enterprise systems, the proposed system enables dynamic risk monitoring by connecting to real-time financial data streams via API interfaces. The system preprocesses and extracts feature from historical financial statements and cash flow records, which are then used to train the RF model using Bagging for sampling and cross-validation for parameter optimization. Upon receiving real-time input, the model generates risk predictions—classified into low, medium, or high levels—along with potential risk source analysis. It then activates a dynamic early warning mechanism if risks exceed preset thresholds and provides tailored prevention strategies based on industry knowledge. Experimental results show that the system achieves an F1 score of 0.8425 and an average accuracy exceeding 80%, demonstrating significant improvements in prediction performance and operational efficiency. This research contributes a robust, real-time, and automated solution for corporate financial risk management.</p>

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Financial risk identification and prevention system based on random forest algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月13日

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  • Bingjie Wang

摘要

As the complexity of corporate financial management increases, conventional financial risk identification methods suffer from limitations in real-time responsiveness, accuracy, and automation, often relying on static data and manual analysis. This study addresses these shortcomings by proposing a real-time financial risk identification and prevention system based on the random forest (RF) algorithm. Unlike prior models, which typically focus on static historical data and lack integration with live enterprise systems, the proposed system enables dynamic risk monitoring by connecting to real-time financial data streams via API interfaces. The system preprocesses and extracts feature from historical financial statements and cash flow records, which are then used to train the RF model using Bagging for sampling and cross-validation for parameter optimization. Upon receiving real-time input, the model generates risk predictions—classified into low, medium, or high levels—along with potential risk source analysis. It then activates a dynamic early warning mechanism if risks exceed preset thresholds and provides tailored prevention strategies based on industry knowledge. Experimental results show that the system achieves an F1 score of 0.8425 and an average accuracy exceeding 80%, demonstrating significant improvements in prediction performance and operational efficiency. This research contributes a robust, real-time, and automated solution for corporate financial risk management.