Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities

Aliya Bayakhmetova, Lyudmila Rudenko, Liubov Krylova, Buldyryk Suleimenova, Shakizada Niyazbekova and Ardak Nurpeisova ()
Additional contact information
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
Ardak Nurpeisova: Department of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical Research University, Astana 010000, Kazakhstan

JRFM, 2025, vol. 18, issue 3, 1-17

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.

Keywords: decision-making; marginalized groups; regional disparities; financial ecosystem; machine learning; financial inclusion; financial literacy (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/18/3/159/pdf (application/pdf)
https://www.mdpi.com/1911-8074/18/3/159/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:18:y:2025:i:3:p:159-:d:1613946

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().