Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.
To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development...
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| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318491 |
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| author | Xini Fang |
| author_facet | Xini Fang |
| author_sort | Xini Fang |
| collection | DOAJ |
| description | To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial risk, and human resource risk are selected as the primary indicators for enterprise risk assessment. Secondly, the Criteria Importance Through Intercriteria Correlation (CRITIC) weight method is employed to determine the importance of these risk indicators, thereby enhancing the model's prediction ability and stability. Following this, the FCM clustering algorithm is utilized for pre-processing sample data to improve the efficiency and accuracy of data classification. Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. The experimental results show that the model achieves an F1 score of 87.26%, an accuracy of 87.95%, an Area under the Curve (AUC) of 91.20%, a precision of 89.29%, and a recall of 87.48%. They are respectively 6.45%, 4.45%, 5.09%, 4.81%, and 3.83% higher than the traditional RF model. In this study, an improved RF model based on FCM clustering is successfully constructed, and the accuracy of risk early warning models and their ability to handle complex data are significantly improved. |
| format | Article |
| id | doaj-art-1d994f2c8f5c4d11b3fe362016d68dbd |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1d994f2c8f5c4d11b3fe362016d68dbd2025-08-20T03:08:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031849110.1371/journal.pone.0318491Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.Xini FangTo enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial risk, and human resource risk are selected as the primary indicators for enterprise risk assessment. Secondly, the Criteria Importance Through Intercriteria Correlation (CRITIC) weight method is employed to determine the importance of these risk indicators, thereby enhancing the model's prediction ability and stability. Following this, the FCM clustering algorithm is utilized for pre-processing sample data to improve the efficiency and accuracy of data classification. Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. The experimental results show that the model achieves an F1 score of 87.26%, an accuracy of 87.95%, an Area under the Curve (AUC) of 91.20%, a precision of 89.29%, and a recall of 87.48%. They are respectively 6.45%, 4.45%, 5.09%, 4.81%, and 3.83% higher than the traditional RF model. In this study, an improved RF model based on FCM clustering is successfully constructed, and the accuracy of risk early warning models and their ability to handle complex data are significantly improved.https://doi.org/10.1371/journal.pone.0318491 |
| spellingShingle | Xini Fang Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering. PLoS ONE |
| title | Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering. |
| title_full | Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering. |
| title_fullStr | Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering. |
| title_full_unstemmed | Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering. |
| title_short | Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering. |
| title_sort | early warning strategies for corporate operational risk a study by an improved random forest algorithm using fcm clustering |
| url | https://doi.org/10.1371/journal.pone.0318491 |
| work_keys_str_mv | AT xinifang earlywarningstrategiesforcorporateoperationalriskastudybyanimprovedrandomforestalgorithmusingfcmclustering |