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Countering Cybercrime Risks in Financial Institutions: Forecasting Information Trends

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  • Aleksandra Kuzior

    (Department of Applied Social Sciences, Faculty of Organization and Management, Silesian University of Technology, 41-800 Zabrze, Poland
    Oleg Balatskyi Department of Management, Sumy State University, 40000 Sumy, Ukraine)

  • Paulina Brożek

    (JSofteris, 41-219 Sosnowiec, Poland)

  • Olha Kuzmenko

    (Economic Cybernetics Department, Sumy State University, 40000 Sumy, Ukraine)

  • Hanna Yarovenko

    (Economic Cybernetics Department, Sumy State University, 40000 Sumy, Ukraine
    Computer Science and Engineering Department, Carlos III University of Madrid, Leganés, 28911 Madrid, Spain)

  • Tetyana Vasilyeva

    (Department of Financial Technologies and Entrepreneurship, Sumy State University, 40000 Sumy, Ukraine
    The London Academy of Science and Business, London W1U 6TU, UK)

Abstract

This article aims to forecast the information trends related to the most popular cyberattacks, seen as the cyber-crimes’ consequences reflecting on the Internet. The study database was formed based on online users’ search engine requests regarding the terms “Cyberattacks on the computer systems of a financial institution”, “Cyberattacks on the network infrastructure of a financial institution”, and “Cyberattacks on the cloud infra-structure of a financial institution”, obtained with Google Trends for the period from 16 April 2017 to 4 October 2022. The authors examined the data using the Z-score, the QS test, and the method of differences of average levels. The data were found to be non-stationary with outliers and a seasonal component, so exponential smoothing was applied to reduce fluctuations and clarify the trends. As a result, the authors built additive and multiplicative cyclical and trend-cyclical models with linear, exponential, and damped trends. According to the models’ quality evaluation, the best results were shown by the trend-cyclic additive models with an exponential trend for predicting cyberattacks on computer systems and the cloud infrastructure and a trend-cyclic additive model with a damped tendency for predicting cyberattacks on the network infrastructure. The obtained results indicate that the U.S. can expect cybercrimes in the country’s financial system in the short and medium term and develop appropriate countermeasures of a financial institution to reduce potential financial losses.

Suggested Citation

  • Aleksandra Kuzior & Paulina Brożek & Olha Kuzmenko & Hanna Yarovenko & Tetyana Vasilyeva, 2022. "Countering Cybercrime Risks in Financial Institutions: Forecasting Information Trends," JRFM, MDPI, vol. 15(12), pages 1-22, December.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:613-:d:1005827
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      Cited by:

      1. Oana-Alexandra SARCEA (MANEA), 2024. "AI & Cybersecurity-connection, impacts, way ahead," International Conference on Machine Intelligence & Security for Smart Cities (TRUST) Proceedings, Smart-EDU Hub, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 1, pages 17-26, July.
      2. Dariusz Krawczyk & Viktoriya Martynets & Yuliia Opanasiuk & Ihor Rekunenko, 2023. "Socio-Economic Development of European Countries in Times of Crisis: Ups and Downs," Sustainability, MDPI, vol. 15(20), pages 1-18, October.

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