Enterprise Risk Assessment Based on Machine Learning

Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the app...

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Autores principales:	Boning Huang, Junkang Wei, Yuhong Tang, Chang Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/620ea76fc01e473f9d114e88b092e7ed
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spelling	oai:doaj.org-article:620ea76fc01e473f9d114e88b092e7ed2021-11-29T00:57:12ZEnterprise Risk Assessment Based on Machine Learning1687-527310.1155/2021/6049195https://doaj.org/article/620ea76fc01e473f9d114e88b092e7ed2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6049195https://doaj.org/toc/1687-5273Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the application of machine learning technology in enterprise risk assessment. According to the existing literature, this paper uses three machine learning algorithms, i.e., random forest (RF), support vector machine (SVM), and AdaBoost, to evaluate enterprise risk. In the specific implementation, the enterprise’s risk assessment indexes are first established, which comprehensively describe the various risks faced by the enterprise through a number of parameters. Then, the three types of machine learning algorithms are trained based on historical data to build a risk assessment model. Finally, for a set of risk indicators obtained under current conditions, the risk index is output through the risk assessment model. In the experiment, some actual data are used to analyze and verify the method, and the results show that the proposed three types of machine learning algorithms can effectively evaluate enterprise risks.Boning HuangJunkang WeiYuhong TangChang LiuHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Boning Huang Junkang Wei Yuhong Tang Chang Liu Enterprise Risk Assessment Based on Machine Learning
description	Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the application of machine learning technology in enterprise risk assessment. According to the existing literature, this paper uses three machine learning algorithms, i.e., random forest (RF), support vector machine (SVM), and AdaBoost, to evaluate enterprise risk. In the specific implementation, the enterprise’s risk assessment indexes are first established, which comprehensively describe the various risks faced by the enterprise through a number of parameters. Then, the three types of machine learning algorithms are trained based on historical data to build a risk assessment model. Finally, for a set of risk indicators obtained under current conditions, the risk index is output through the risk assessment model. In the experiment, some actual data are used to analyze and verify the method, and the results show that the proposed three types of machine learning algorithms can effectively evaluate enterprise risks.
format	article
author	Boning Huang Junkang Wei Yuhong Tang Chang Liu
author_facet	Boning Huang Junkang Wei Yuhong Tang Chang Liu
author_sort	Boning Huang
title	Enterprise Risk Assessment Based on Machine Learning
title_short	Enterprise Risk Assessment Based on Machine Learning
title_full	Enterprise Risk Assessment Based on Machine Learning
title_fullStr	Enterprise Risk Assessment Based on Machine Learning
title_full_unstemmed	Enterprise Risk Assessment Based on Machine Learning
title_sort	enterprise risk assessment based on machine learning
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/620ea76fc01e473f9d114e88b092e7ed
work_keys_str_mv	AT boninghuang enterpriseriskassessmentbasedonmachinelearning AT junkangwei enterpriseriskassessmentbasedonmachinelearning AT yuhongtang enterpriseriskassessmentbasedonmachinelearning AT changliu enterpriseriskassessmentbasedonmachinelearning
_version_	1718407648525680640

Enterprise Risk Assessment Based on Machine Learning

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