Application of Machine Learning in Enterprise Risk Management

Jia, Dian; Wu, Zhaoyang

doi:10.1155/2022/4323150

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…For example, Company P is an enterprise with only a few hundred employees, and its system compilation is complex, with 15 articles, 154 chapters, and more than 400,000 words, and it is ready to be further expanded. Such a compilation of management systems, even corporate executives may not have the patience and time to read them through [16]. The employees' attention and understanding of the management system also determine its performance effect.…”

Section: 24mentioning

confidence: 99%

Enterprise Management Resource Protection System Based on Digital Information Technology

Zhou

2022

Wireless Communications and Mobile Computing

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Enterprise management resource protection is a system management activity in which an enterprise conducts comprehensive planning, allocation, utilization, and development of its resources, and it is the process of organizing, planning, coordinating, supervising, and controlling the allocation, utilization, and development of enterprise resources. The purpose of this paper is to analyze the system that digital information technology applies to enterprise management by constructing models and combinations based on digital information technology so that the built models and combinations have a more effective analysis and research. This paper first gives a general introduction to digital information technology, then analyzes the theory of enterprise management resource protection, then establishes a model for information technology to act on enterprise management, and finally analyzes the combination of information technology and enterprise management and compares the two through enterprise case analysis. The experimental results show that in the case of enterprise management in various regions, the efficiency of enterprise management based on digital information technology is obviously higher, and the enterprise resources can be better protected. From 2018 to 2021, the development of enterprise management resource protection in various regions is basically showing an upward trend; it increased from 3.443, 5.414, 6.473, 4.382, 2.997, 3.751, 0.506, and 2.974 in 2018 to 6.187, 7.658, 8.601, 7.518, 5.932, 6.516, 3.877, and 6.243 in 2021. This is a good demonstration of the effectiveness of the protection of enterprise management resources based on digital information technology.

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Section: 24mentioning

confidence: 99%

Enterprise Management Resource Protection System Based on Digital Information Technology

Zhou

2022

Wireless Communications and Mobile Computing

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“…The ERM theory facilitates the risk monitoring process since it involves the use of a risk assessment process to evaluate the probability and impacts of certain risks (Jia & Wu, 2022;Obondi, 2022). The ERM theory helps in the risk audits which should be done regularly as the project progresses to have the capacity to monitor and evaluate their effect on the project (Mahama, Elbashir, Sutton, & Arnold, 2022).…”

Section: Enterprise Risk Management Theorymentioning

confidence: 99%

Risk Management Practices and Performance of Mining Projects in Kwale County, Kenya

MUTHUI,

KISING’U, PhD

2024

strategicjournals.com

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The purpose of this research was to examine the influence of risk management practices on the performance of mining projects in Kwale County, Kenya. Specifically, the research examined the influence of risk identification practice and risk analysis practice on the performance of mining projects in Kwale County, Kenya. The theoretical framework consisted of the enterprise risk management theory, systems theory, contingency theory and complexity theory. Drawing on the positivist research philosophy, the research utilized the quantitative non-experimental research methodology. The research employed the correlational cross-sectional survey design for testing noncausal relationships among variables. The proportionate stratified random sampling technique was used to select a sample size of 128 project management staff from a target population of 188 project management staff in charge of the mining projects of the 7 mining companies in Kwale County, Kenya. A pilot study was conducted to test the validity and reliability of the constructed survey questionnaire. A self-administered structured questionnaires was used to collect primary data. The research utilized the cross-sectional survey-based approach. Through the drop and pick method, the survey questionnaires were hand delivered to the project management staff of the mining companies. The collected data was processed and entered into the statistical package for social sciences (SPSS) version 26 to create a data sheet used for statistical analysis. The Pearson's correlation results indicated that risk identification practice and risk analysis practice had positive and significant relationship with performance of mining projects in Kwale County, Kenya. The multiple regression results showed that risk identification practice and risk analysis practice had positive and significant influence on performance of mining projects in Kwale County, Kenya. The mining project managers should recognize the importance of risk management practices and apply them to their projects to minimize risks for successful projects in the mining industry. The policy makers should initiate policy review to motivate project managers recognize the importance of risk management practices.

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“…Machine learning is an interdisciplinary domain that integrates the knowledge of multiple domains including probability theory and algorithm complexity theory. The core of machine learning is artificial intelligence [1].…”

Section: Application Of Machine Learning In Financial Risk Managementmentioning

confidence: 99%

Application for Machine Learning Methods in Financial Risk Management

Liu

2024

HSET

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Financial risk management has significant importance and implications for individuals, businesses, investors, and even the whole nation. As the financial markets and institutes grow complex so does the risk associated with financial management. The spectrum of financial risks includes market risk, liquidity risk, credit risk, and a range of others. With a multitude of portfolios and sophisticated products, financial firms require apt tools that can accurately measure the risk, returns, and exposure. The growing complexity has also made statistical and simulation tools ineffective and there is a growing emergence of machine learning. Machine learning is a sub-category of artificial intelligence that uses algorithms. These algorithms analyze data and then learn from it so that a decision based on a certain experience or criteria can be made. Machine language tools provide data protection as the information is only accessible to the key decision-makers. Deep learning is modeled like a human brain and therefore it operates using multiple layers of artificial networks and can process and use a very vast amount of data.

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Application of Machine Learning in Enterprise Risk Management

Cited by 7 publications

References 32 publications

Enterprise Management Resource Protection System Based on Digital Information Technology

Enterprise Management Resource Protection System Based on Digital Information Technology

Risk Management Practices and Performance of Mining Projects in Kwale County, Kenya

Application for Machine Learning Methods in Financial Risk Management

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