Cargando…
Forecasting of digital financial crimes in Russia based on machine learning methods
In the modern world, economic relations, business, and markets are increasingly being transferred to the online world. Accordingly, the percentage of a new type of financial fraud – digital crimes – is also growing. In Russia, they are one of the leaders in economic crimes. Constant development and...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Paris
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159233/ http://dx.doi.org/10.1007/s11416-023-00480-3 |
_version_ | 1785037092474060800 |
---|---|
author | Ivanyuk, Vera |
author_facet | Ivanyuk, Vera |
author_sort | Ivanyuk, Vera |
collection | PubMed |
description | In the modern world, economic relations, business, and markets are increasingly being transferred to the online world. Accordingly, the percentage of a new type of financial fraud – digital crimes – is also growing. In Russia, they are one of the leaders in economic crimes. Constant development and improvement of digital technologies make for the emergence of increasingly sophisticated types of digital fraud. Information security management systems are among the most important parts of both public and private internal policy of the enterprises that use them. Every year, the issue of combating cyber attacks is becoming more acute. For example, in 2021, the volume of transactions without the consent of bank customers increased by 38.8% compared to 2020. For cyber risks not to lead to such serious consequences, it is necessary to determine the influence of external factors on the dynamics of digital financial crimes in Russia. We will develop a predictive system based on machine learning methods to predict the number of digital financial crimes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11416-023-00480-3. |
format | Online Article Text |
id | pubmed-10159233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Paris |
record_format | MEDLINE/PubMed |
spelling | pubmed-101592332023-05-09 Forecasting of digital financial crimes in Russia based on machine learning methods Ivanyuk, Vera J Comput Virol Hack Tech Original Paper In the modern world, economic relations, business, and markets are increasingly being transferred to the online world. Accordingly, the percentage of a new type of financial fraud – digital crimes – is also growing. In Russia, they are one of the leaders in economic crimes. Constant development and improvement of digital technologies make for the emergence of increasingly sophisticated types of digital fraud. Information security management systems are among the most important parts of both public and private internal policy of the enterprises that use them. Every year, the issue of combating cyber attacks is becoming more acute. For example, in 2021, the volume of transactions without the consent of bank customers increased by 38.8% compared to 2020. For cyber risks not to lead to such serious consequences, it is necessary to determine the influence of external factors on the dynamics of digital financial crimes in Russia. We will develop a predictive system based on machine learning methods to predict the number of digital financial crimes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11416-023-00480-3. Springer Paris 2023-05-04 /pmc/articles/PMC10159233/ http://dx.doi.org/10.1007/s11416-023-00480-3 Text en © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Paper Ivanyuk, Vera Forecasting of digital financial crimes in Russia based on machine learning methods |
title | Forecasting of digital financial crimes in Russia based on machine learning methods |
title_full | Forecasting of digital financial crimes in Russia based on machine learning methods |
title_fullStr | Forecasting of digital financial crimes in Russia based on machine learning methods |
title_full_unstemmed | Forecasting of digital financial crimes in Russia based on machine learning methods |
title_short | Forecasting of digital financial crimes in Russia based on machine learning methods |
title_sort | forecasting of digital financial crimes in russia based on machine learning methods |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10159233/ http://dx.doi.org/10.1007/s11416-023-00480-3 |
work_keys_str_mv | AT ivanyukvera forecastingofdigitalfinancialcrimesinrussiabasedonmachinelearningmethods |