首页    期刊浏览 2024年11月06日 星期三
登录注册

文章基本信息

  • 标题:The use of accounting information for assessing the economic security of commercial banks
  • 本地全文:下载
  • 作者:Anastasia Gontar ; Vladimir Plotnikov ; Nadezda Chernovanova
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2019
  • 卷号:91
  • 页码:1-5
  • DOI:10.1051/e3sconf/20199108065
  • 出版社:EDP Sciences
  • 摘要:The authors studied the use of accounting information for assessing the level of economic security of commercial banks. The article substantiates the choice of financial indicators of credit institutions as input parameters of the neural network. The analytical platform Deductor Studio Academic was chosen as the instrumental environment for assessing the level of economic security of banks. With its help, cluster analysis was performed. The division of the initial set of credit institutions into subsets (economic security classes) used the k-means method with splitting into three clusters: cluster 0 - “Optimal level of economic security”, cluster 1 -“Conditionally optimal level of economic security”, cluster 2 - “Low level of economic security”. The proposed data model with the results of the division of credit institutions into clusters according to the financial indicators of International Financial Reporting Standards made it possible to draw conclusions about the level of economic security of credit institutions and justify the choice of accounting information indicators for implementing a neural network approach to banking research.
  • 其他摘要:The authors studied the use of accounting information for assessing the level of economic security of commercial banks. The article substantiates the choice of financial indicators of credit institutions as input parameters of the neural network. The analytical platform Deductor Studio Academic was chosen as the instrumental environment for assessing the level of economic security of banks. With its help, cluster analysis was performed. The division of the initial set of credit institutions into subsets (economic security classes) used the k-means method with splitting into three clusters: cluster 0 - “Optimal level of economic security”, cluster 1 -“Conditionally optimal level of economic security”, cluster 2 - “Low level of economic security”. The proposed data model with the results of the division of credit institutions into clusters according to the financial indicators of International Financial Reporting Standards made it possible to draw conclusions about the level of economic security of credit institutions and justify the choice of accounting information indicators for implementing a neural network approach to banking research.
国家哲学社会科学文献中心版权所有