Considerations Related To The Application Of Deep Learning And Neural Networks In Finance And Banking. A Bibliometric Approach

Daniela Iulia Maria Carbune ()
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Daniela Iulia Maria Carbune: University of Craiova ”Eugeniu Carada” Doctoral School of Economic Sciences, Romania

Ovidius University Annals, Economic Sciences Series, 2024, vol. XXIV, issue 2, 480-488

Abstract: The aim of this paper is to examine the impact and applications of Deep Learning (DL) and Neural Networks (NN) in financial markets and especially on banking markets, using a bibliometric analysis based on 970 articles published between 2015-2024. The study highlights key trends, collaboration networks and influential contributions using data extracted from the Web of Science database and analyzed with VOSviewer software. Findings illustrate the widespread integration of those two AI applications in financial approaches such as credit evaluation, stock market forecasting and risk modeling. The study highlights the prominence of areas such as computer science and business economics while pointing out the need for hybrid models and explainable AI, essential for meeting regulatory compliance. Additionally, it emphasizes the interdisciplinary nature of this field, connecting AI with big data and financial systems.

Keywords: deep learning; neural networks; banking; bibliometric analysis; VOSviewer (search for similar items in EconPapers)
JEL-codes: G10 G17 G20 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xxiv:y:2024:i:2:p:480-488

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