Artificial Intelligence and Finance: A bibliometric review on the Trends, Influences, and Research Directions.

Q2 Pharmacology, Toxicology and Pharmaceutics

F1000Research Pub Date : 2025-01-23 eCollection Date: 2025-01-01 DOI:10.12688/f1000research.160959.1

Prasenjit Roy, Biswajit Ghose, Premendra Kumar Singh, Pankaj Kumar Tyagi, Asokan Vasudevan

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引用次数: 0

Abstract

Background: This bibliometric study examines the intersection of artificial intelligence (AI) and finance, providing a comprehensive analysis of its evolution, central themes, and avenues for further exploration. The study aims to uncover the theoretical foundations, methodological approaches, and practical implications of AI in financial contexts.

Methods: The research employs bibliometric techniques, using 607 Web of Science (WoS) indexed papers. The Theory-Context-Characteristics-Methodology (TCCM) framework guides the analysis, focusing on thematic mapping to explore key topics. Core areas such as risk management, market efficiency, and innovation are analyzed, alongside emerging themes like ethical AI, finance applications, and factors influencing AI-driven financial decision-making.

Results: The findings reveal critical gaps in interdisciplinary methods, ethical considerations, and methodological advancements necessary to develop robust and transparent AI systems. Thematic mapping highlights the increasing importance of ethical AI practices and the influence of AI on financial decision-making processes. Emerging research areas emphasize the need for innovative frameworks and solutions to address current challenges.

Conclusions: This study provides valuable insights for academics, industry practitioners, and policymakers to harness transformative potential of AI in finance. This research offers a foundation for future studies and practical applications by addressing key gaps and promoting interdisciplinary and ethical approaches in a rapidly evolving field.

查看原文本刊更多论文

人工智能与金融：趋势、影响和研究方向的文献计量学综述。

背景：本文献计量学研究考察了人工智能（AI）与金融的交叉，对其演变、中心主题和进一步探索的途径进行了全面分析。该研究旨在揭示人工智能在金融背景下的理论基础、方法方法和实际意义。方法：采用文献计量学方法，对607篇被Web of Science （WoS）收录的论文进行分析。理论-语境-特征-方法论（TCCM）框架指导分析，侧重于主题映射以探索关键主题。分析了风险管理、市场效率和创新等核心领域，以及道德人工智能、金融应用以及影响人工智能驱动的金融决策的因素等新兴主题。结果：研究结果揭示了跨学科方法、伦理考虑和方法进步方面的关键差距，这些都是开发强大而透明的人工智能系统所必需的。专题地图强调了道德人工智能实践的日益重要性以及人工智能对财务决策过程的影响。新兴研究领域强调需要创新的框架和解决方案来应对当前的挑战。结论：本研究为学者、行业从业者和政策制定者利用人工智能在金融领域的变革潜力提供了有价值的见解。该研究通过解决关键差距和促进跨学科和伦理方法在快速发展的领域为未来的研究和实际应用奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

F1000Research Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)

CiteScore

5.00

自引率

0.00%

发文量

1646

审稿时长

1 weeks

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