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Updated: Oct 12, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Enterprise Risk Assessment Based on Machine Learning.

Boning Huang¹, Junkang Wei², Yuhong Tang³

¹Shenzhen University Webank Institute of Fintech, Shenzhen University, Shenzhen 518052, China.

Computational Intelligence and Neuroscience

|November 26, 2021

Summary

This summary is machine-generated.

Machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, effectively evaluate enterprise risks. These models utilize historical data and risk indexes for accurate risk assessment, ensuring business development.

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Area of Science:

Business Analytics
Computational Intelligence
Data Science

Background:

Enterprise risk assessment is crucial for sustainable business development.
Machine learning (ML) offers advanced capabilities for data prediction and risk evaluation.
Existing methods may not fully capture the complexity of modern enterprise risks.

Purpose of the Study:

To investigate the application of ML in enterprise risk assessment.
To evaluate the effectiveness of Random Forest (RF), Support Vector Machine (SVM), and AdaBoost algorithms for risk evaluation.
To develop and validate an ML-based risk assessment model.

Main Methods:

Established comprehensive enterprise risk assessment indexes.
Trained RF, SVM, and AdaBoost ML algorithms using historical enterprise data.
Developed risk assessment models to output risk indexes based on current indicators.

Main Results:

The developed ML models demonstrated effective enterprise risk evaluation capabilities.
Experimental analysis using actual data validated the performance of the proposed algorithms.
All three algorithms (RF, SVM, AdaBoost) showed significant potential in assessing enterprise risks.

Conclusions:

Machine learning algorithms provide a robust framework for scientific enterprise risk assessment.
RF, SVM, and AdaBoost are suitable for building accurate and effective enterprise risk assessment models.
The proposed ML approach enhances the ability of enterprises to manage and mitigate risks.