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Article

Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities

by
Aliya Bayakhmetova
1,
Lyudmila Rudenko
2,
Liubov Krylova
3,
Buldyryk Suleimenova
4,
Shakizada Niyazbekova
5,6 and
Ardak Nurpeisova
7,*
1
School of Entrepreneurship and Innovation, Almaty Management University, Almaty 050000, Kazakhstan
2
Department of Economic Theory, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
3
Department of World Economy and World Finance, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
4
Department of Computer Science, Faculty of Sciences and Technology, Yessenov University, Aktau 130000, Kazakhstan
5
Department of Banking and Monetary Regulation, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
6
Research and Education Center ‘Sustainable Development’, Moscow Witte University, Moscow 115432, Russia
7
Department of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical Research University, Astana 010000, Kazakhstan
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(3), 159; https://doi.org/10.3390/jrfm18030159
Submission received: 25 January 2025 / Revised: 25 February 2025 / Accepted: 6 March 2025 / Published: 17 March 2025
(This article belongs to the Special Issue Financial and Sustainability Reporting in a Digital Era, 2nd Edition)
Browse Figures
Review Reports Versions Notes

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.

1. Introduction

Artificial intelligence (AI) is increasingly influencing financial behavior and decision-making, transforming traditional paradigms in both individual and corporate finance. AI-based consulting robots are changing traditional money management by offering automated investment platforms (Shanmuganathan, 2020). The use of artificial intelligence in behavioral finance is aimed at reducing psychological biases and improving the accuracy of financial decisions (Singh et al., 2025). Advanced artificial intelligence systems, including large language models and anomaly detection methods, can analyze financial behavior, predict budgets, and provide personalized recommendations to promote responsible financial management (Musto et al., 2015; Devan et al., 2023; Talasila, 2024).
In the corporate sector, AI is rapidly transforming core financial functions such as risk assessment, stock trading, and lending processes (Mayor, 2020; Saheb et al., 2023; Du et al., 2025). Despite these advances, significant ethical challenges remain, especially regarding algorithmic bias, privacy, and fairness, which must be addressed to ensure transparency and fairness of AI models (Chopra, 2024). Moreover, the potential of AI to promote financial literacy and inclusivity, especially among low-income and marginalized groups, is poorly understood (Kumar, 2025; Singh et al., 2025). Regional differences in AI adoption further highlight the need to adapt tools to different markets and expand global financial participation. (Goodell et al., 2021).
This study uses bibliometric analysis, a quantitative method widely used to assess research trends and identify gaps in the literature (Zupic & Čater, 2014; Barra et al., 2024). By mapping scientific contributions, intellectual trends, and new opportunities at the intersection of AI and financial behavior, bibliometric analysis offers a comprehensive understanding of the evolution of this field. In particular, this study attempts to answer the following questions:
1. What are the main trends and thematic developments in the field of integrating AI into financial behavior research?
2. Who is making the biggest contribution, and which collaborative networks are driving this field?
3. What gaps and opportunities exist for future research, especially in the field of promoting financial inclusion and addressing ethical issues?
The purpose of this study is to provide a comprehensive overview of the impact of artificial intelligence on financial behavior, while simultaneously considering risks, identifying new opportunities, and providing ideas for interdisciplinary collaboration to optimize financial decision-making and ensure equity.

2. Literary Review

The integration of artificial intelligence into financial behavior research has brought transformative changes, rethinking traditional methods and introducing innovative tools for decision-making and risk assessment (Huang & Haifeng, 2022; Masood, 2024). AI technologies such as machine learning, deep learning, and neural networks enable accurate financial modeling, behavioral model analysis, and personalized recommendations, enhancing efficiency and strategic planning in finance (Gao et al., 2024; Yarabolu & Sriharsha, 2025).
Machine learning algorithms, widely used to process large datasets and predict market trends, have revolutionized financial modeling by uncovering hidden patterns and eliminating cognitive biases such as herd behavior (Singh et al., 2025). Similarly, deep learning and neural networks improve the accuracy of predicting borrower behavior and market dynamics, offering reliable solutions for risk assessment and portfolio optimization (Weinjing, 2025; Saheb et al., 2023). Natural language processing (NLP) further expands the capabilities of AI by analyzing unstructured financial text, allowing real-time analysis of the disorder and predicting the impact on the market (Mayor, 2020; Du et al., 2025).
Ethical and regulatory issues. Despite its benefits, the integration of AI into financial systems raises significant ethical concerns, including algorithmic bias, lack of transparency, and data privacy issues (Chopra, 2024; Adeyelu et al., 2024). The regulatory framework remains fragmented, requiring proactive approaches to balance innovation and consumer protection. Transparent algorithm certification, robust data security protocols, and cross-disciplinary collaboration are critical to addressing these challenges (Sushkova & Minbaleev, 2021; Truby et al., 2020).
AI for financial inclusion. AI tools have the potential to improve financial literacy and inclusion, especially among marginalized populations. For example, AI-powered platforms can provide personalized financial education and microfinance solutions, reducing socioeconomic gaps and increasing global financial justice (Kumar, 2025; Goodell et al., 2021). However, this area remains poorly understood, which requires research in the field of customized AI solutions for different communities.
Recent research has made significant strides in studying financial behavior and its determinants, including bibliometric analysis and AI applications. (Ahmed et al., 2022; Alavi et al., 2024; Shi et al., 2025) They presented a bibliometric and systematic review of financial literacy and behavior, highlighting the dynamic evolution of these areas, but leaving the role of AI unexplored (Ingale & Paluri, 2022). Similarly, they compared the impact of financial literacy on financial behavior, but their study overlooked the transformative potential of AI in shaping behavior through advanced tools and techniques. The work of Viale et al. (2023) delves deeper into the applications of AI in finance, assessing its impact on financial well-being and regulatory implications. However, their analysis is primarily of a practical nature and does not have a bibliometric perspective. Together, these studies highlight the growing interest in financial behavior and AI but reveal a gap in understanding their intersection from a bibliometric perspective. To address this issue, the current study offers a comprehensive bibliometric analysis to identify trends, key participants, and emerging topics in integrating AI into financial behavior research.

3. Materials and Methods

The methodological basis of this study is based on observation, measurement, and comparison, which form the basis of the general scientific approach of this study, further expanding it with a systematic approach and bibliometric analysis (Donthu et al., 2021).
To study research trends and ideas at the intersection of artificial intelligence and financial behavior, bibliometric analysis was applied using the Biblioshiny graphical interface of the Bibliometrix R package (Say et al., 2024), which allows a comprehensive display of research trends, key contributions and thematic developments in this field. The main stages of bibliometric analysis are shown in Figure 1 (Passas, 2024).
The Scopus database was chosen as the main source of bibliometric data due to its unprecedented reach, interdisciplinary focus, and advanced analytical tools. It features peer-reviewed journals, conference proceedings, and books on various subject areas, which ensure high quality and reliability of data (Suresh & Pradhan, 2023). Scopus surpasses WoS in the breadth of indexed content, including a wider range of regional journals and new research. Compared to Google Scholar, Scopus provides more thorough data analysis, which reduces the risk of including non-academic or low-quality sources.
To search for the relevant literature, the keywords “artificial intelligence”, “financial behavior” and the logical operator “And” were used to search for intersections of artificial intelligence and financial behavior for the period from 1987 to 2024.
Although artificial intelligence has developed significantly in recent years, early research provides critical insight into conceptual and technological evolution. Seminal studies such as those by Lebaron et al. (1999) and Bahrammirzaee (2010) highlight early applications in financial behavior, including stock market modeling and financial forecasting based on neural networks. These studies remain relevant today (Oyeniyi et al., 2024), which justifies the inclusion of historical studies to account for the progress of AI in financial decision-making.
Selecting only two keywords narrowed the scope of the dataset and excluded studies that used related terms. The exclusion of non-English studies could have led to the omission of valuable contributions from non-English-speaking regions and, therefore, there were no language restrictions. The articles corresponding to these keywords were extracted from Scopus in BibTeX format. As a result, 1019 documents were selected.
This volume was reduced to 294 documents in the process due to the introduction of inclusion and exclusion criteria. Books, conference materials, reviews, and book chapters were excluded from the selection process. To ensure the integrity of the dataset, duplicate and irrelevant records were also deleted. The selection area includes only the results of scientific research in the form of articles, which, as noted by Ramos-Rodriguez and Ruiz-Navarro (2004), are regarded as “certified knowledge”.
The bibliometric analysis consisted of performance analysis (publication trends, most cited papers, leading authors, and influential journals) and scientific mapping (keyword matching, citation network analysis, and thematic clustering). Clustering of coincidences was used to identify the dominant research topics, which revealed the main trends in financial behavior based on artificial intelligence and ethical aspects such as transparency and confidentiality.

4. Results

Descriptive statistics. Table 1 provides descriptive statistics that provide a comprehensive overview of the integration of artificial intelligence into financial behavior.
The period from 1987 to 2024 shows an increase in scientific interest, which is confirmed by the diversity of 294 sources. The largest number of documents falls on such subject areas as Computer Science—187, Engineering—108, Business, Management, and Accounting—83, Economics, Econometrics, and Finance—81, Mathematics—60, Social Sciences—58, and Decision Science—31.
The annual growth rate of 13.46% indicates a consistent and steady increase in the number of publications over time. In 1987, 1 article was published, in 2007, 2 articles, in 2017, 7 articles, and in 2024, 107 articles. The average document age of 4.93 years illustrates the relative youth of this research area, indicating that the field is in its developing stage. The annual scientific output is shown in Figure 2.
The analysis showed that the use of AI in financial behavior began to gain significant popularity after 2016. Exponential growth has been observed since 2016, driven by advances in artificial intelligence technologies and their applications in the financial sector. The thematic comparison also highlights the emergence of areas such as NLP-based sentiment analysis and artificial intelligence-based risk assessment models.
The statistics shown in Table 1 also focus on cooperation in the field under study, as evidenced by 3.46 co-authors per article and 30.51% international cooperation. The trend towards teamwork and collective accumulation of knowledge is confirmed by a relatively small proportion of documents written by one author (13.08%).
Most influential authors.
Citation analysis revealed the most influential articles and authors (Table 2).
The analysis of the authors’ contributions based on fractionated articles reveals significant information about the joint dynamics and intellectual productivity in the context of artificial intelligence and financial behavior research. The largest number of articles belongs to Zhang Y., with a total of 7, but a fractionated contribution of 2.36, indicating significant collaboration with other authors. Similarly, Li J. and Boustani N.M., with fractionated contributions of 1.83 and 1.50, respectively, demonstrate active co-authorship, reflecting the interdisciplinary nature of the field (Table 2). The differences between the total number of articles and fractionated articles highlight the important role of fractionated metrics in accurately representing researchers’ intellectual contributions, which is important for future researchers. Of additional interest is information on fundamental articles that have contributed to the field under study.
The most significant articles: The key academic contributions of the most significant articles in the field of integrating artificial intelligence into behavioral finance and their global impact are visualized in Figure 3.
In particular, the works by Lebaron et al. (1999) and Bahrammirzaee (2010) are classified as highly effective contributions, with 462 and 391 references to the document. The first paper, published in the Journal of Economic Dynamics and Control, highlights its fundamental importance and continuing relevance in the field. Lebaron et al. (1999) presented the results of an experimental stock market simulation where artificial intelligence algorithms played the role of traders. The author of the second paper, Bahrammirzaee, published his article in the journal Neural Computing and Applications. Bahrammirzaee (2010) compared the application of artificial neural networks, expert systems and hybrid intelligence systems in financial areas such as credit assessment, portfolio management, and financial forecasting and planning. This subject remains relevant to this day (Oyeniyi et al., 2024).
The study by the authors shown in Figure 3 covers a wide range of aspects of the use of artificial intelligence and technologies to improve analysis and decision-making. These include feature engineering strategies for fraud detection (Bahnsen et al., 2016), analysis of economic dynamics using search query data (Preis et al., 2010), the role of artificial intelligence in building smart cities (Yigitcanlar et al., 2020), and the use of fuzzy logic in decision-making in relation to suppliers (Orji & Wei, 2015), issues related to decision-making and management of clean energy production (Jahangir & Soltani, 2019). Todd and Benbasat (1999) evaluate the impact of decision support systems on the choice of strategies, and Königstorfer and Thalmann (2020) explores the use of artificial intelligence in commercial banks. These works make a significant contribution to the understanding and development of financial behavior through artificial intelligence.
Recent publications such as Bussmann et al. (2021) in Computational Economics show rapid growth in citations, reflecting the growing interest in computational economics and AI-based solutions in financial decision-making. Mathematical and physical modeling approaches play a central role in certain areas of research in AI and financial systems, as evidenced by the significant number of citations to the paper Preis et al. (2010).
The distribution of citations highlights the central role of seminal works in shaping the theoretical and methodological foundations. At the same time, the presence of recently published and frequently cited papers indicates the growing dynamism and evolving research directions of AI and financial behavior. This highlights the importance of fundamental research as well as the rapid adoption of new methodologies to address contemporary problems in the financial sector.
The most frequently used terms are shown in Figure 4.
The treemap visualization highlights “artificial intelligence” as the most significant research focus, accounting for 35% of the total, further highlighting its fundamental role in the field of financial behavior. As the field continues to be of interest in academic circles due to its broad issues related to investment behavior patterns, institutional decisions, and supporting the efficient functioning of financial markets (Ahmed et al., 2022; Alavi et al., 2024; Zhavoronok et al., 2022).
A comprehensive visualization of the relationship between keywords in the analyzed literature corpus is presented in Figure 5.
The central position of “artificial intelligence” as a dominant node indicates the additional unifying role of AI in various research areas.
Close connections of “artificial intelligence” are shown with the terms “financial markets”, “decision making”, and “finance”, emphasizing the integration of AI into financial systems and its application in optimizing decision-making processes. The connection with “learning systems”, “big data”, and “neural networks” emphasizes the technological basis on which AI applications are built in this area.
Even though the terms “sustainable development” and “automation” are not central, they point to new trends in which AI intersects with broader socio-economic issues.
Keywords such as “crime” and “anomaly detection” have fewer connections, but suggest opportunities for further study, especially in understanding the role of AI in reducing risks and fraud in financial systems.
Figure 6 shows a thematic map reflecting a structured overview of research topics classified by relevance and future direction. Within the framework of this thematic map, future research topics are related to crime, the Internet of Things, and automation in the financial sector and in behavioral finance. Fraud issues in the financial sector and automation in behavioral finance are interesting due to the development of technology and existing gaps in human digital literacy (Königstorfer & Thalmann, 2020; Zeng & Chen, 2023; Gui et al., 2024; Shoetan et al., 2024). The prospects of the designated research vectors are further confirmed by the conducted factorial analysis (Figure 7).
The factorial analysis presented in Figure 7 reveals topic clusters at the intersection of artificial intelligence and financial behavior. The dimensions explaining 49.69% (Dim 1) and 17.94% (Dim 2) of the variance highlight the key relationships between keywords and their topic distribution. Note that Dim 1 and Dim 2 explain 67.63% of the variance, making the graph a robust representation of topic relationships. These dimensions serve as principal components summarizing the relationships between keywords and topic clusters.
Keywords are scattered throughout the graph without a single dense central cluster. Instead, there are several localized groups or independent clusters along two dimensions. Keywords such as decision-making, financial markets, and machine learning are in the lower-right cluster. This cluster represents the dominant topics in AI and financial behavior research, focusing on technical applications such as predictive modeling, risk assessment, and decision optimization.
The upper-right quadrant highlights technological advances in AI, focusing on data mining and neural networks that are used to assess risks, detect fraud (crime), and minimize financial losses. These topics represent operational priorities in financial systems where computational methods are applied to enhance security, accuracy, and efficiency.
In contrast, the upper-left quadrant reflects more behavioral and automated topics. This quadrant suggests a growing interest in understanding and influencing individual financial actions such as spending and saving, as well as automating financial workflows to improve decision-making efficiency. The relative isolation of consumer behavior indicates a gap in the integration of behavioral research with technical and operational AI applications, which opens up opportunities for future research.
Finally, the lower-left quadrant contains keywords such as “female”, “human”, “article”, and “United States”, which highlight sociological and regional aspects in the study of financial behavior. These keywords suggest a limited study of demographic and regional differences in belief in the AI system and its impact on financial decision-making. The presence of the “article” indicates the need for meta-analytical studies assessing publication trends, collaboration networks, and gaps in this area.
The factorial analysis revealed a diverse and evolving research landscape with well-established topics, new computational methods, and significant gaps in behavioral and demographic dimensions. By addressing these gaps, future research can ensure that the transformative potential of AI in financial systems is used ethically and inclusively, contributing to a more equitable and efficient global financial ecosystem.
The visualization in Figure 8 highlights dominant terms such as “artificial intelligence”, “machine learning”, and “fintech”, showing their central role in the research landscape and confirming the importance of journals such as IEEE Access and the Journal of Behavioral and Experimental Finance.
Table 3 provides information on the most relevant sources.
This table illustrates the diverse but interconnected nature of publication platforms in this field, highlighting the different roles that journals play—some as prolific (such as IEEE ACCESS), and others as specialized, high-impact sources (such as the Journal of Behavioral and Experimental Finance). Expert Systems with Applications is also included in the key journals. High performance and impact indicators make them the main sources for future literature reviews and references. As noted earlier in this article, the subject of the study is interdisciplinary and collaborative. The network diagram (Figure 9) illustrates global patterns of research collaboration, with China, the United States, Great Britain, and India becoming global centers of academic exchange. These countries demonstrate strong relationships with many other countries, which indicates their key role in facilitating international research cooperation, especially in the field of artificial intelligence and related disciplines.
The global distribution of research efforts is shown in Figure 10. Stanford University, Griffith University, and Tianjin University are notable centers of excellence in AI and finance.
The combination of institutions and keywords revealed a strong correlation between thematic areas such as “deep learning”, “big data”, and “neural networks”, reflecting regional and institutional priorities in advancing technological innovation.
AI-based financial recommendations can be disproportionately beneficial to institutional investors because they have access to large amounts of data, potentially putting retail investors at a disadvantage. In addition, the opacity of some artificial intelligence models (black box algorithms) exacerbates information asymmetry, as investors may not fully understand the logic of recommendations generated using artificial intelligence. Moreover, excessive reliance on AI can enhance herd behavior, increasing market volatility. Addressing these challenges requires regulatory oversight, increased AI transparency, and investor education to ensure equal access to financial assistance.
Despite its advantages in reducing psychological biases in financial decision-making, AI also leads to the emergence of new biases that influence investor behavior (Shanmuganathan, 2020). The study identified three key behavioral biases associated with AI-based financial decision-making:
1. Excessive self-confidence. The high accuracy of predictive models based on artificial intelligence can lead investors to overestimate their ability to make profitable decisions, leading to excessive risk and potential financial losses.
2. Accessibility bias. Sentiment analysis tools based on artificial intelligence often highlight the latest financial data and trends, which can lead to investors overly focusing on short-term changes while ignoring long-term market fundamentals.
3. A penchant for automation. The growing reliance on AI-based financial recommendations leads to blind reliance on algorithmic conclusions, where investors can follow AI-generated suggestions without independent verification.
Recognizing these shortcomings is important when developing financial instruments based on artificial intelligence that reduce such risks by increasing transparency, explainability, and adaptive learning models. Solving these problems requires the development of artificial intelligence systems that encourage human control and ensure interpretability of financial decision-making processes.
Artificial intelligence and internal control mechanisms in management accounting. In addition to financial decision-making, artificial intelligence also plays a crucial role in improving internal control mechanisms (Wang, 2024; Chung & Lin, 2023; Ramos-Rodriguez & Ruiz-Navarro, 2004) within the framework of management accounting. Internal control systems are complemented by and contribute to the following:
1. Improving the accuracy of financial data. Financial reporting systems based on artificial intelligence reduce errors and improve real-time financial analysis.
2. More effective risk management and audit. Artificial intelligence-based tools can detect anomalies, fraud patterns, and inconsistencies in financial statements, which helps strengthen compliance with legal requirements.
3. Improved integration of management control and strategic planning. Decision support systems enhanced by artificial intelligence enable strategic financial planning based on data, aligning business goals with risk assessments and performance indicators.
Given the close relationship between internal control and management control, artificial intelligence-driven internal audit systems can significantly improve the accuracy and efficiency of financial decision-making, ensuring greater financial integrity and compliance.

5. Discussion

The bibliometric analysis conducted highlights significant trends and research patterns at the intersection of artificial intelligence and financial behavior. While these results provide a comprehensive overview of the evolution of the field, they also reveal critical opportunities for future research and practical applications. The presence of keywords such as “machine learning” and “financial inclusion” indicate a growing interest in the application of AI to address inequalities in access to financial services.
The keyword match analysis revealed three main thematic groups: AI and decision-making, inclusive finance, and ethical and regulatory issues of AI.
Future research could focus on developing AI-based tools tailored to marginalized populations, such as personalized financial education platforms and microfinance solutions optimized by predictive algorithms.
With the advent of technologies such as natural language processing (NLP) and AI-powered chatbots, there is a clear opportunity to bridge the financial literacy gap. Integrating these technologies into educational programs can help people better understand complex financial concepts, reduce cognitive biases, and improve decision-making.
The analysis also highlights the importance of addressing ethical concerns, including algorithmic bias and data privacy. Research into developing transparent and fair AI models, especially in sensitive areas such as lending and credit scoring, remains an under-explored opportunity with significant practical implications.
Bibliometric data highlights regional differences in research inputs and collaboration patterns, highlighting the need to explore how AI tools can be adapted for use in underrepresented or under-resourced regions. Such efforts have the potential to improve global financial justice and reduce socioeconomic inequality.
The clustering of terms related to decision-making and behavioral modeling suggests a promising direction for using AI to identify and mitigate behavioral biases in financial behavior.

6. Conclusions

This article provides a comprehensive bibliometric description of the impact of artificial intelligence on financial behavior, and offers a systematic analysis of research trends, collaboration networks, and thematic gaps. Unlike previous studies that focused on specific AI applications, this study focuses on ethical considerations, financial inclusion, and regional differences, highlighting under-explored areas that are critical to responsibly integrating AI into finance.
The empirical analysis provides a comprehensive overview of research trends and contributions at the intersection of artificial intelligence and financial behavior. Analyzing 294 documents published between 1985 and 2024, the study shows a steady annual growth rate of 13.34%, reflecting the growing relevance of this new field. With an average document age of 4.93 years and 18.85 citations per article, the study demonstrates significant academic impact. The international co-authorship rate is 30.51%, which highlights the global nature of research in this field. The average number of co-authors per paper is 3.46, which indicates close collaboration between researchers.
This study used bibliometric analysis, which is widely recognized for mapping scientific fields and achieving its research goals. The growing popularity of this method is due to its ability to systematize a large volume of scientific literature and identify dynamic opportunities and key issues within the subject area of research (Zupic & Čater, 2014; Ellegaard & Wallin, 2015; Rialti et al., 2019; Donthu et al., 2021; Greener, 2022; Surekha et al., 2024; Boran et al., 2024).
By identifying key trends, thematic developments, and collaboration networks, the study successfully solved its research objectives. The systematization of research in the field of financial behavior also allowed us to structure an overview of how AI has been integrated into financial behavior research over time. By identifying key trends, themes, and influential research, it has contributed to a deeper understanding of how AI has influenced financial decision-making, which highlights the hallmark of this research. The results confirm that it significantly improves the accuracy of financial decision-making and expands access to financial advisory services. In addition, the study highlights the importance of algorithm transparency and robust data privacy policies to build trust in AI-driven financial systems. However, despite these achievements, a number of problems remain, including data bias and legislative issues that need to be addressed in order to ensure the ethical and fair integration of AI into financial decision-making, which lays the foundation for future theoretical work.
To address these challenges, the following areas should be prioritized in future research and policy development: improvement of the regulatory framework and strengthening governance structures to ensure the fair, transparent and accountable application of artificial intelligence in financial behavior.
Audit mechanisms and the creation of systematic audit systems to assess algorithmic distortions, errors, and fairness in financial decision-making models.
Improving AI literacy. Expanding financial literacy initiatives to train consumers and financial professionals to implement AI responsibly and reduce the risks associated with AI-based financial instruments.
Ensuring inclusivity and developing AI models adapted to financially underprivileged and marginalized communities, ensuring equal access to AI-based financial services.
This study provides practical recommendations for key stakeholders. It is recommended that financial regulators develop ethical guidelines on AI and oversight mechanisms to ensure responsible implementation of AI in financial markets. For investors, it encourages the use of AI decision-making tools that allow for human control, preventing excessive reliance on automated systems. To developers, it encourages the focus on developing transparent, interpretable models and reducing the risks associated with bias in financial decision-making.
This study has a number of limitations. Firstly, bibliometric methods mainly capture quantitative trends and do not provide a deep qualitative understanding of financial decision-making. Secondly, the analysis is limited to publications indexed by Scopus. Future research should include interviews with experts, case studies, or econometric modeling to complement the bibliometric data and offer a more holistic view of the role of artificial intelligence in financial behavior.
In addition, the study highlights key trends such as the use of machine learning, artificial intelligence-based risk assessment, and decision-making processes. Distinguished authors such as Zhang Y and LiJ, as well as influential journals including IEEE Access, have been recognized, along with strong global collaborations reflected in co-authorship models. China, the USA, Great Britain, and India are becoming global centers of academic exchange. These countries demonstrate strong relationships with many other countries, which indicates their key role in facilitating international research cooperation, especially in the field of artificial intelligence and related disciplines. The keyword match analysis revealed three main thematic groups: AI and decision-making, inclusive finance, and ethical and regulatory issues of AI.
The study also identifies critical gaps. These include ethical considerations, regional differences in AI adoption, and the need for strategies to expand access to AI-based financial services. Especially for marginalized groups.
The main purpose of this study is to provide a structured overview of the impact of artificial intelligence on financial behavior and identify opportunities for future research. The goal has been achieved. The findings provide a clear roadmap for advancing AI-based financial decision-making while addressing key ethical and regulatory issues. Ensuring algorithmic transparency, trust, and fairness in applications remains a priority, along with developing AI-based tools to reduce economic inequality and improve financial literacy.
By addressing these gaps, future research can help create a more equitable and inclusive financial ecosystem, ensuring that AI technologies serve diverse populations while respecting ethical standards and regulations. In the future, it is necessary to further explore the relationship between ethics and financial regulation and management accounting in order to create reliable financial management systems based on AI.

Author Contributions

Conceptualization, A.B., L.K., L.R., B.S., A.N. and S.N.; methodology, A.B., L.K., L.R. and A.N.; formal analysis, A.B., L.K. and L.R.; investigation, A.B., B.S., A.N. and S.N.; resources, A.B., L.K., L.R., B.S., A.N. and S.N.; writing, original draft preparation, A.B., A.N. and S.N.; writing, review and editing, A.N. and S.N.; visualization, B.S., A.N. and S.N.; supervision, A.B., L.K., A.N. and S.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

The study did not involve humans.

Data Availability Statement

The data is contained in the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The main stages of bibliometric analysis.
Figure 1. The main stages of bibliometric analysis.
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Figure 2. Annual scientific production.
Figure 2. Annual scientific production.
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Figure 3. Most global cited documents.
Figure 3. Most global cited documents.
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Figure 4. TreeMap.
Figure 4. TreeMap.
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Figure 5. Co-occurrence network.
Figure 5. Co-occurrence network.
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Figure 6. Thematic map.
Figure 6. Thematic map.
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Figure 7. Factorial analysis.
Figure 7. Factorial analysis.
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Figure 8. Authors—keywords—sources.
Figure 8. Authors—keywords—sources.
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Figure 9. Collaboration network.
Figure 9. Collaboration network.
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Figure 10. Institutions—countries—keywords.
Figure 10. Institutions—countries—keywords.
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Table 1. Descriptive statistics of the bibliographic collection.
Table 1. Descriptive statistics of the bibliographic collection.
DescriptionResults
Timespan1985:2024
Sources (Journals, Books, etc.)294
Documents 390
Annual growth rate %13.46
Document average age 4.93
Average citations per doc18.85
Average citations per year per doc2.883
References 1
Keywords Plus (ID)2561
Author’s keywords (DE)1427
Authors1268
Author appearances 1348
Authors of single-authored docs 51
Single-authored docs 51
Documents per author 0.308
Co-authors per doc 3.46
International co-authorships % 30.51
Table 2. The most significant authors.
Table 2. The most significant authors.
AuthorsArticlesArticles Fractionalized
Zhang Y.72.36
Li J.41.83
Boustani N.M.21.50
Chen A.P.31.33
Wang X.31.25
Zhang S.21.20
Liu Y.21.17
Zhou H.51.01
Ceron B.M.21.00
Manahov V.21.00
Table 3. The most relevant sources.
Table 3. The most relevant sources.
SourceNo. of Papers Pub.H-IndexG-IndexTotal No. of Citations
IEEE ACCESS18917299
Expert Systems with Applications888506
Journal of Behavioral and Experimental Finance444308
Applied Soft Computing Journal 33376
Decision Support Systems33366
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MDPI and ACS Style

Bayakhmetova, A.; Rudenko, L.; Krylova, L.; Suleimenova, B.; Niyazbekova, S.; Nurpeisova, A. Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities. J. Risk Financial Manag. 2025, 18, 159. https://doi.org/10.3390/jrfm18030159

AMA Style

Bayakhmetova A, Rudenko L, Krylova L, Suleimenova B, Niyazbekova S, Nurpeisova A. Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities. Journal of Risk and Financial Management. 2025; 18(3):159. https://doi.org/10.3390/jrfm18030159

Chicago/Turabian Style

Bayakhmetova, Aliya, Lyudmila Rudenko, Liubov Krylova, Buldyryk Suleimenova, Shakizada Niyazbekova, and Ardak Nurpeisova. 2025. "Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities" Journal of Risk and Financial Management 18, no. 3: 159. https://doi.org/10.3390/jrfm18030159

APA Style

Bayakhmetova, A., Rudenko, L., Krylova, L., Suleimenova, B., Niyazbekova, S., & Nurpeisova, A. (2025). Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities. Journal of Risk and Financial Management, 18(3), 159. https://doi.org/10.3390/jrfm18030159

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