Artificial Intelligence in Financial Behavior: Bibliometric Ideas and New Opportunities
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".