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An Overview Over The Impact Of Artificial Intelligence Technologies In The Banking Industry – A Bibliometric Analysis

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  • CARBUNE DANIELA IULIA MARIA

    (UNIVERSITY OF CRAIOVA, EUGENIU CARADA DOCTORAL SCHOOL OF ECONOMIC SCIENCES)

Abstract

The banking industry is experiencing a significantly transformation fueled by the adoption of artificial intelligence (AI) technologies, which are reshaping essential operations, improving service quality and encouraging innovation across the sector. This paper conducts a bibliometric analysis to assess the impact of artificial intelligence technologies within financial institutions, applying VOSviewer software to identify and visualize the trends and patterns in the academic literature extracted from the Web of Science database. Present study highlights the development of research themes, key collaboration networks and finds out the interconnections through various AI applications within the baking sector. The findings of the study underscore the fact that artificial intelligence plays as a protagonist in increasing operational efficiency, enhancing customer experiences and streamlining decision-making workflows. By highlighting the current applications of artificial intelligence and identifying areas where it has demonstrated its effectiveness, this information significantly contributes to a deeper and more comprehensive understanding of the role of AI in reshaping banking processes and services. At the same time, the research article offers promising directions for future research, such as exploring the ethical implications of AI, addressing possible biases in algorithmic models and investigating ways to optimally integrate with existing systems. Advancing research in these directions will be essential for fully exploiting the potential of AI and managing the challenges inherent in its adoption, thus contributing to a continuous, responsible and sustainable innovation of the banking industry in the face of technological change.

Suggested Citation

  • Carbune Daniela Iulia Maria, 2024. "An Overview Over The Impact Of Artificial Intelligence Technologies In The Banking Industry – A Bibliometric Analysis," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 204-212, December.
    • Handle: RePEc:cbu:jrnlec:y:2024:v:6i:p:204-212
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    References listed on IDEAS

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    1. Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.
    2. Farrukh Rafiq & Nikhil Dogra & Mohd Adil & Jei-Zheng Wu, 2022. "Examining Consumer’s Intention to Adopt AI-Chatbots in Tourism Using Partial Least Squares Structural Equation Modeling Method," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    3. Tzeremes, Nickolaos G., 2015. "Efficiency dynamics in Indian banking: A conditional directional distance approach," European Journal of Operational Research, Elsevier, vol. 240(3), pages 807-818.
    4. Constantin Zopounidis & Emilios C. C Galariotis & Michael Doumpos & Stavroula Sarri & Kostas Andriosopoulos, 2015. "Multiple criteria decision aiding for finance: An updated bibliographic survey," Post-Print hal-02879842, HAL.
    5. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    6. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
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