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Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities

Author

Listed:
  • Aliya Bayakhmetova

    (School of Entrepreneurship and Innovation, Almaty Management University, Almaty 050000, Kazakhstan)

  • Lyudmila Rudenko

    (Department of Economic Theory, Financial University under the Government of the Russian Federation, Moscow 125167, Russia)

  • Liubov Krylova

    (Department of World Economy and World Finance, Financial University under the Government of the Russian Federation, Moscow 125167, Russia)

  • Buldyryk Suleimenova

    (Department of Computer Science, Faculty of Sciences and Technology, Yessenov University, Aktau 130000, Kazakhstan)

  • Shakizada Niyazbekova

    (Department of Banking and Monetary Regulation, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
    Research and Education Center ‘Sustainable Development’, Moscow Witte University, Moscow 115432, Russia)

  • Ardak Nurpeisova

    (Department of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical Research University, Astana 010000, Kazakhstan)

Abstract

Artificial intelligence is transforming financial behavior and decision-making processes, offering new opportunities to optimize financial systems and reduce bias. This study explores the intersection of AI and financial behavior using bibliometric analysis to identify trends, gaps, and emerging directions in this rapidly evolving field. A total of 1019 documents are available in Scopus for the period 1987–2024. The articles are analyzed using the Bibliometrix R package and the Bibliophagy graphical user interface. Key findings show a robust annual growth rate of 13.34%, highlighting the growing relevance of the topic. The analysis revealed central themes such as machine learning, decision-making, and financial inclusion, along with critical gaps in ethical considerations, regional disparities, and practical applications of AI for marginalized populations. Leading contributors and influential sources, including journals such as IEE Access and Expert Systems with Applications, were mapped to understand the intellectual structure of the field. The study highlights the urgent need to address and mitigate algorithmic biases to ensure fairness, transparency, and ethical outcomes in AI-driven systems. It also highlights the importance of improving financial literacy and adapting AI tools for fair financial inclusion. These insights provide a roadmap for future research and practical innovation, ensuring that AI is integrated into financial systems ethically and effectively to promote a more inclusive global financial ecosystem.

Suggested Citation

  • Aliya Bayakhmetova & Lyudmila Rudenko & Liubov Krylova & Buldyryk Suleimenova & Shakizada Niyazbekova & Ardak Nurpeisova, 2025. "Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities," JRFM, MDPI, vol. 18(3), pages 1-17, March.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:3:p:159-:d:1613946
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    References listed on IDEAS

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    1. Peter Todd & Izak Benbasat, 1999. "Evaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection," Information Systems Research, INFORMS, vol. 10(4), pages 356-374, December.
    2. Servet Say & Mesut Dogan & Daulen Abdeshov & Murat Tekbas & Levent Sezal & Burhan Erdoğan, 2024. "Evolution of Financial Development Research: A Bibliometric Analysis," JRFM, MDPI, vol. 18(1), pages 1-19, December.
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