IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i12p556-d1540897.html
   My bibliography  Save this article

Incorporating Artificial Intelligence into Finance: A Bibliometric Analysis

Author

Listed:
  • Antonio Carlos Alcázar-Blanco

    (Department of Financial Economics, University of Extremadura, 52139 Plasencia, Spain)

  • José Francisco Rangel-Preciado

    (Department of Economics, University of Extremadura, 52308 Plasencia, Spain)

  • Fiama Portillo-Santos

    (Department of Economics, University of Extremadura, 52308 Plasencia, Spain)

Abstract

The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI in finance has grown significantly, which shows the evolution and importance of this area of research. Eight main topics were obtained in this area: bank, prediction, impact, decision, valuesstock, genetic algorithm, big data analysis, and social data analysis. This study shows us how the incorporation of AI can strongly support the analysis of different financial situations such as decision making or the prediction of movements.

Suggested Citation

  • Antonio Carlos Alcázar-Blanco & José Francisco Rangel-Preciado & Fiama Portillo-Santos, 2024. "Incorporating Artificial Intelligence into Finance: A Bibliometric Analysis," JRFM, MDPI, vol. 17(12), pages 1-14, December.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:12:p:556-:d:1540897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/12/556/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/12/556/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manjunath Padigar & Ljubomir Pupovac & Ashish Sinha & Rajendra Srivastava, 2022. "The effect of marketing department power on investor responses to announcements of AI-embedded new product innovations," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1277-1298, November.
    2. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
    2. HUO, Peng & WANG, Luxin, 2022. "Digital economy and business investment efficiency: Inhibiting or facilitating?," Research in International Business and Finance, Elsevier, vol. 63(C).
    3. Darko B. Vuković & Senanu Dekpo-Adza & Stefana Matović, 2025. "AI integration in financial services: a systematic review of trends and regulatory challenges," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-29, December.
    4. Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).
    5. Li Xian Liu & Zhiyue Sun & Kunpeng Xu & Chao Chen, 2024. "AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges," IJFS, MDPI, vol. 12(3), pages 1-35, June.
    6. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Ayed Alwadain & Rao Faizan Ali & Amgad Muneer, 2023. "Estimating Financial Fraud through Transaction-Level Features and Machine Learning," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
    8. Wang, Dan & Chen, Zhi & Florescu, Ionuţ & Wen, Bingyang, 2023. "A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating," Research in International Business and Finance, Elsevier, vol. 64(C).
    9. Wang, Liang & Li, Munan, 2024. "An exploration method for technology forecasting that combines link prediction with graph embedding: A case study on blockchain," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    10. Robert Kudelić & Tamara Šmaguc & Sherry Robinson, 2025. "Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-43, December.
    11. Goodell, John W. & Oriani, Marco Ercole & Paltrinieri, Andrea & Patel, Ritesh, 2023. "The importance of ABS 2 journals in finance scholarship: Evidence from a bibliometric case study," Finance Research Letters, Elsevier, vol. 55(PA).
    12. Minghao Zhou & Wenhao Lai, 2023. "Coal gangue recognition based on spectral imaging combined with XGBoost," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-15, January.
    13. Alena Vagaská & Miroslav Gombár & Antonín Korauš, 2022. "Mathematical Modeling and Nonlinear Optimization in Determining the Minimum Risk of Legalization of Income from Criminal Activities in the Context of EU Member Countries," Mathematics, MDPI, vol. 10(24), pages 1-25, December.
    14. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    15. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    16. Pavlos I. Zitis & Stelios M. Potirakis & Alex Alexandridis, 2024. "Forecasting Forex Market Volatility Using Deep Learning Models and Complexity Measures," JRFM, MDPI, vol. 17(12), pages 1-22, December.
    17. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    18. Yasmeen Ansari & Mansour Saleh Albarrak & Noorjahan Sherfudeen & Arfia Aman, 2022. "A Study of Financial Literacy of Investors—A Bibliometric Analysis," IJFS, MDPI, vol. 10(2), pages 1-16, May.
    19. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    20. Anica-Popa Liana-Elena & Vrîncianu Marinela & Petrică Papuc Iuliana-Mădălina, 2023. "AI – powered Business Services in the Hyperautomation Era," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1036-1050, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:17:y:2024:i:12:p:556-:d:1540897. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.