A Research on Enterprise Technical Risk Threshold Activation Model Construction in ICV Industry

Addressing the critical need for enhanced industrial risk monitoring, this research advances the analytical capabilities of management entities and policy advisors in scrutinizing enterprise technological risks in specific sectors. It introduces a machine learning-assisted approach to systematically comprehend the triggers and mitigators of technological risks. The research develops a Machine Learning-based Enterprise Technology Risk Threshold Activation (ETRTA) Model. The model, grounded in a multi-dimensional classification of enterprise risks, is adept at delving into the nuances of these risks in industry-specific contexts. Employing a suite of eight machine learning techniques, including Random Forest, XGBoost, etc. the model trains on various parameters to discern the characteristics of enterprise technological risks. Additionally, automated processes are employed to uncover consistent patterns in the activation of these risks. The efficacy of the model is highlighted by the classification prediction accuracy of three gradient boosting ensemble models, which stands at 82.59%. The accuracy facilitates the identification of enterprises at potential technological risk using extensive datasets. The future scope includes enhancing the prediction precision and robustness of the models and broadening their applicability in assessing enterprise technological risks in diverse industries.