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Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM
1School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, China.
Plos One
|March 11, 2025
Summary
This study introduces an improved risk early warning system using Fuzzy C-Means (FCM) clustering and Random Forest (RF) models. The novel approach enhances enterprise risk assessment accuracy and speed for better financial stability.
Area of Science:
- Financial Risk Management
- Data Science and Machine Learning
- Enterprise Risk Assessment
Background:
- Traditional risk early warning systems face challenges in accuracy and response speed.
- Effective enterprise risk assessment requires robust models to analyze diverse risk indicators.
Purpose of the Study:
- To develop a novel, accurate, and fast risk early warning system.
- To improve enterprise risk assessment by integrating advanced machine learning techniques.
Main Methods:
- Utilized Fuzzy C-Means (FCM) clustering for data pre-processing and classification.
- Employed the Criteria Importance Through Intercriteria Correlation (CRITIC) method for risk indicator weighting.
- Developed an optimized Random Forest (RF) model for enhanced prediction capabilities.
Main Results:
- The proposed model achieved an F1 score of 87.26%, accuracy of 87.95%, and AUC of 91.20%.
- Performance metrics showed significant improvements (4.45%-6.45%) compared to the traditional RF model.
- Demonstrated superior accuracy and efficiency in handling complex financial data for risk prediction.
Conclusions:
- The integration of FCM clustering and an optimized RF model significantly enhances risk early warning system performance.
- The developed system offers a more accurate and stable approach to enterprise risk assessment.
- This study provides a valuable framework for improving financial risk management through advanced data analytics.
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