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Design of Financial and Economic Monitoring System Based on Big Data Clustering

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Advanced Hybrid Information Processing (ADHIP 2022)

Abstract

Aiming at the problem that the selection of financial and economic risk indicators is not comprehensive, resulting in excessive financial and economic operational risks, a financial and economic monitoring system is designed based on big data clustering. Financial and economic risks are risks that are formed and accumulated in the financial system in the process of economic cyclical and financial unbalanced development. According to its formation mechanism, complex network models are used to analyze the dynamic correlation of financial and economic risks. Select financial and economic risk indicators based on big data clustering, and strengthen the supervision of high-risk financial sub-markets such as stocks and foreign exchange markets. Establish a financial and economic monitoring system from three aspects of financial and economic revenue and expenditure, debt and external risks, and jointly determine the development trend of financial and economic risks. The test results show that the financial and economic monitoring system based on big data clustering can reduce the value at risk of operational risk, namely VaR value, and promote the balanced development of the financial market.

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Funding

Jiangsu Province “14th Five-Year” Business Administration Key Construction Discipline Project (SJYH2022–2/285).

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Correspondence to Kaili Wang .

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, K., Chen, Y. (2023). Design of Financial and Economic Monitoring System Based on Big Data Clustering. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-28787-9_32

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  • DOI: https://doi.org/10.1007/978-3-031-28787-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28786-2

  • Online ISBN: 978-3-031-28787-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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