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Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm

With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial servi...

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Autor principal: Tian, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481309/
https://www.ncbi.nlm.nih.gov/pubmed/36120675
http://dx.doi.org/10.1155/2022/7964123
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author Tian, Lei
author_facet Tian, Lei
author_sort Tian, Lei
collection PubMed
description With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial services and management platforms cannot effectively alleviate the current financial crisis. In the face of increasingly complex financial risks, traditional financial service and management platforms cannot achieve effective information sharing, which leads to continued low service and management efficiency and frequent financial risk problems. Support vector machine is a data classification algorithm based on supervision, which can realize data sharing and improve the efficiency of data processing. The article firstly readjusted the underlying architecture of the financial service and management platform to break through the barriers of data interaction. Then on this basis, the article further combines the support vector machine algorithm and extends it from binary data classification to multivariate classification. Finally, the paper redesigns the financial service and management platform considering support vector machines. After a series of experiments, it can be found that the financial service and management platform based on the support vector machine algorithm can reduce the financial risk by 17.2%, improve the financial service level by 30.2%, and improve the financial comprehensive service level by 45.2%. At the same time, thanks to information sharing and interaction, the financial service and management platform can effectively predict financial risks, and the accuracy of the prediction basically reaches 78.9%. This shows that a financial service and management platform that takes into account the support vector machine algorithm can effectively prevent financial risks and improve the efficiency of financial services and management.
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spelling pubmed-94813092022-09-17 Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm Tian, Lei Comput Intell Neurosci Research Article With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial services and management platforms cannot effectively alleviate the current financial crisis. In the face of increasingly complex financial risks, traditional financial service and management platforms cannot achieve effective information sharing, which leads to continued low service and management efficiency and frequent financial risk problems. Support vector machine is a data classification algorithm based on supervision, which can realize data sharing and improve the efficiency of data processing. The article firstly readjusted the underlying architecture of the financial service and management platform to break through the barriers of data interaction. Then on this basis, the article further combines the support vector machine algorithm and extends it from binary data classification to multivariate classification. Finally, the paper redesigns the financial service and management platform considering support vector machines. After a series of experiments, it can be found that the financial service and management platform based on the support vector machine algorithm can reduce the financial risk by 17.2%, improve the financial service level by 30.2%, and improve the financial comprehensive service level by 45.2%. At the same time, thanks to information sharing and interaction, the financial service and management platform can effectively predict financial risks, and the accuracy of the prediction basically reaches 78.9%. This shows that a financial service and management platform that takes into account the support vector machine algorithm can effectively prevent financial risks and improve the efficiency of financial services and management. Hindawi 2022-09-09 /pmc/articles/PMC9481309/ /pubmed/36120675 http://dx.doi.org/10.1155/2022/7964123 Text en Copyright © 2022 Lei Tian. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Tian, Lei
Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm
title Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm
title_full Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm
title_fullStr Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm
title_full_unstemmed Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm
title_short Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm
title_sort design and implementation of financial service and management platform considering support vector machine algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481309/
https://www.ncbi.nlm.nih.gov/pubmed/36120675
http://dx.doi.org/10.1155/2022/7964123
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