Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear authors, after reading your work I have the following recommendations that maybe could help you to improve this work.

The paper covers an interesting and relevant topic with the propose to explore the intersection of AI and financial behavior using bibliometric analysis to identify trends, gaps and emerging directions. As such, the paper could have a place in JRFM editorial line.

The title is concise, specific, and relevant.

The abstract it is well written, and we can understand the question addressed in a broad context, the purpose of the study, the main method and the main findings and contributions.

The keywords are specific to the article.

Is clear the reading of the text (textual coherence and cohesion). 

The paper cites an appropriate range of literature sources, namely recent publications.

I don’t see in the paper strategic management accounting but accounting information in general. As such, I suggest the authors highlight the importance and impact of internal control for the quality of information in terms of management accounting and management control. There is a strong relationship between internal control and management control and management audit.

The reading of the text is clear (textual coherence and cohesion).

The literature review focuses the relevant topics for the study.

In the methodology the authors must reveal in more detail the methodology adopted and situate the study on the time. It’s important describe the methodology with sufficient detail to allow others to replicate and build on published results.  Why the publications chosen for this investigation were between 1987 and 2024? AI is a very recent thematic and included in the analysis not recent articles may disturb the obtained results.

In the conclusion the authors must develop the limitations of the study (which are many), improve the theoretical and practical contributions and explain the originality of paper. The authors must respond exhaustively to the research questions formulated in the introduction.

I hope that my comments and suggestions can help to improve the paper.

I wish all the best to the authors!

Author Response

Comments 1:
Dear authors, after reading your work I have the following recommendations that maybe could help you to improve this work.
The paper covers an interesting and relevant topic with the propose to explore the intersection of AI and financial behavior using bibliometric analysis to identify trends, gaps and emerging directions. As such, the paper could have a place in JRFM editorial line.
The title is concise, specific, and relevant.
The abstract it is well written, and we can understand the question addressed in a broad context, the purpose of the study, the main method and the main findings and contributions.
The keywords are specific to the article.
Is clear the reading of the text (textual coherence and cohesion). 
The paper cites an appropriate range of literature sources, namely recent publications.
I don’t see in the paper strategic management accounting but accounting information in general. As such, I suggest the authors highlight the importance and impact of internal control for the quality of information in terms of management accounting and management control. There is a strong relationship between internal control and management control and management audit.
The reading of the text is clear (textual coherence and cohesion).
The literature review focuses the relevant topics for the study.

Response 1
Dear Reviewer,
thank you for your insightful comments and suggestions that helped improve and strengthen our research. 
The authors of the article agree with the need to focus on the importance and impact of internal control on the quality of information from the point of view of management accounting and management control.
In this regard, the article is supplemented by the following:
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 (Monteiro et al., 2023; Obaydin, 2024) within the framework of management accounting. Internal control systems, complemented by and, contribute to:
Improving the accuracy of financial data. Financial reporting systems based on artificial intelligence reduce errors and improve real-time financial analysis.
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.
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.

Comments 2:

In the methodology the authors must reveal in more detail the methodology adopted and situate the study on the time. It’s important describe the methodology with sufficient detail to allow others to replicate and build on published results.  Why the publications chosen for this investigation were between 1987 and 2024? AI is a very recent thematic and included in the analysis not recent articles may disturb the obtained results.

Response 2:

After reviewing the methodology, the authors ensured that the study was clear and repeatable.

Due to the fact that the outlined methodology already affects the scientific basis of the study, the rationale for bibliometric analysis and the choice of the Scopus database, as well as the explanation of the key stages of the analysis, illustrated in Figure 1, the authors of this article have expanded this part of the article with the following clarifications:

 

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 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.

 

Regarding the time frame (1987 and 2024), the choice has been clarified:

Although artificial intelligence has developed significantly in recent years, early research provides critical insight into conceptual and technological evolution. Seminal works such as Lebaron B. 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.

Comments 3:

In the conclusion the authors must develop the limitations of the study (which are many), improve the theoretical and practical contributions and explain the originality of paper. The authors must respond exhaustively to the research questions formulated in the introduction.

I hope that my comments and suggestions can help to improve the paper.

I wish all the best to the authors!

Response 3:

Thank you for the opportunity to improve the final part. All offers are accepted.

Within the framework of these proposals, adjustments were made to the final part regarding the limitations of the study, the theoretical and practical contributions of the study, and the answers to the research questions, which generally clarified the entire section of the article.

 

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.

 

It was also added:

The systematization of research in the field of financial behavior 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.

 

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. Strengthening governance structures to ensure the fair, transparent and accountable application of artificial intelligence in financial behavior.

 

Audit mechanisms and 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.

The 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: encourage the use of AI decision-making tools that allow for human control, preventing excessive reliance on automated systems. To developers and: focus on developing transparent, interpretable models and to reduce 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. 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 study the relationship between ethics and financial regulation and management accounting in order to create reliable financial management systems based on AI.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

The topic of the paper is actual and relevant. However, please specify the research limitations and future research directions. Also,  give some recommendations for overcoming the disadvantages of using AI in financial behaviour. It would be good to accentuate your original opinion about that. 

Author Response

Comments 1:

Dear Authors,

The topic of the paper is actual and relevant. However, please specify the research limitations and future research directions. Also, give some recommendations for overcoming the disadvantages of using AI in financial behaviour. It would be good to accentuate your original opinion about that. 

Response 1:

 

Dear reviewer, thank you for your thoughtful review and recognition of the relevance of our research. We appreciate your comments and have carefully considered suggestions regarding limitations, future research directions, and recommendations for overcoming the disadvantages of financial behavior related to AI.

The authors would like to point out that the adjustments made in response to Reviewer 1's comments already affect several aspects of your review. In particular: limitations of the study and future directions of research.

Regarding overcoming shortcomings And financial behavior. The authors of the proposed article acknowledge that the introduction of AI into financial behavior is accompanied by certain risks and limitations, such as bias in decision-making, regulatory issues, so the article is expanded on this aspect.

Behavioral biases in AI-based financial decision-making. Despite its advantages in reducing psychological biases in financial decision-making, its use 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 (Hasan, 2022; Sarin, 2023):

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.

And in terms of the practical aspect, it is possible to solve these problems in future research and the development of appropriate policy directions.

To address these challenges, the following areas should be prioritized in future research and policy development:

Improvement of the regulatory framework. Strengthening governance structures to ensure the fair, transparent and accountable application of artificial intelligence in financial behavior;

Audit mechanisms and 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.

The 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: encourage the use of AI decision-making tools that allow for human control, preventing excessive reliance on automated systems. To developers and: focus on developing transparent, interpretable models and to reduce the risks associated with bias in financial decision-making.

Regarding the initial position regarding the role And the final

anse behavior. In the final part of the article, the emphasis is placed on the fact that although AI has significant potential to improve financial decision-making and inclusivity, it needs to be implemented with particular attention to ethics, transparency and regulatory oversight.

The 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: encourage the use of AI decision-making tools that allow for human control, preventing excessive reliance on automated systems. To developers and: focus on developing transparent, interpretable models and to reduce the risks associated with bias in financial decision-making.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. Add about the negative side of the use of AI for financial decisions made by investors in information asymmetry situations.
2. From the collection of literature studies tracked, it can be shown that the most dominant type of financial behavioral bias studied is linked to the use of AI.
3. The implications of the research are less pragmatic and the recommendations submitted have not shown the benefits of the research

Author Response

Comments 1:

Add about the negative side of the use of AI for financial decisions made by investors in information asymmetry situations.

The implications of the research are less pragmatic and the recommendations submitted have not shown the benefits of the research.

Response 1:

Dear reviewer, thank you for your thoughtful feedback and constructive suggestions.

The authors acknowledge the importance of discussing the negative consequences of AI-based financial decision-making, especially in situations of information asymmetry. In response, the authors included an additional discussion in the results section, highlighting which market inefficiency and investor bias may inadvertently increase when learning from incomplete or biased datasets. The conclusions of the study also include practical recommendations on this issue.

The asymmetry of information and Both in the financial decision-making process. Although widely regarded as a tool for improving financial decision-making, it also poses challenges in situations of information asymmetry (Amudha, 2021; Kalina & Dvi, 2022; Alp Coskun, 2023; Imtiaz, 2023). 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.

Comments 2:

From the collection of literature studies tracked, it can be shown that the most dominant type of financial behavioral bias studied is linked to the use of AI.

Response 2:

The discussion section has been revised to clearly highlight the most common financial behavioral biases associated with AI. The analysis identifies: overconfidence, accessibility error, and automation bias.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Research motivation

Artificial intelligence plays a role in significantly accelerating financial decision-making processes. However, there are gaps in algorithmic bias, ethical issues and financial inclusion.  Research gaps regarding regional differences and disadvantaged groups necessitated further consideration of the subject. 

 Aim

 The study discusses the effects of artificial intelligence on financial behavior and thus aims to contribute to the creation of a fair, transparent and inclusive financial ecosystem.

 Method

The study was carried out with bibliometric analysis of 294 out of 1019 articles published in the Scopus database, and Bibliometrix R package and Bibliophagy graphical interface were used. 

Findings

There was an annual growth of 13.34% in the number of publications. Machine learning, decision-making processes and financial inclusion are among the most important topics. On the other hand, ethical issues, regional inequalities and artificial intelligence applications have been found to be inadequate for disadvantaged groups.

Policy Recommendations:

At the end of the study, policy recommendations are presented on issues such as 1- Reducing Algorithmic Biases, 2- Increasing Financial Literacy, 3- Increasing the inclusiveness of artificial intelligence and 4- Strengthening the legal regulatory framework regarding artificial intelligence.

Thus, the publication of the manusript can be benefit current literature.

Author Response

Comments 1:

Research motivation

Artificial intelligence plays a role in significantly accelerating financial decision-making processes. However, there are gaps in algorithmic bias, ethical issues and financial inclusion.  Research gaps regarding regional differences and disadvantaged groups necessitated further consideration of the subject. 

 Aim

 The study discusses the effects of artificial intelligence on financial behavior and thus aims to contribute to the creation of a fair, transparent and inclusive financial ecosystem.

 Method

The study was carried out with bibliometric analysis of 294 out of 1019 articles published in the Scopus database, and Bibliometrix R package and Bibliophagy graphical interface were used. 

Findings

There was an annual growth of 13.34% in the number of publications. Machine learning, decision-making processes and financial inclusion are among the most important topics. On the other hand, ethical issues, regional inequalities and artificial intelligence applications have been found to be inadequate for disadvantaged groups.

Policy Recommendations:

At the end of the study, policy recommendations are presented on issues such as 1- Reducing Algorithmic Biases, 2- Increasing Financial Literacy, 3- Increasing the inclusiveness of artificial intelligence and 4- Strengthening the legal regulatory framework regarding artificial intelligence.

Thus, the publication of the manusript can be benefit current literature.

Response 1:

Dear Reviewer,

Thank you for your detailed feedback and recognition of the relevance of this study.

The authors have carefully reviewed your comments and clarified the purpose of the study.

 

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.

Author Response File: Author Response.docx