In this study, I employ a multifaceted comprehensive scientometric approach to explore the intellectual underpinnings of AI and ML in financial research by examining the publication patterns of articles, journals, authors, institutions, and nations by leveraging quantitative techniques, that transcend conventional systematic literature reviews, enabling the effective analysis of vast scientometric and bibliographic data. By applying these approaches, I identify influential works, seminal contributions, thought leaders, topical clusters, research streams, and new research frontiers, ultimately fostering a deeper understanding of the knowledge structure in AI and ML finance research by considering publication records from 2010 to 2022 from several search engines and database sources. The present study finds a marked increase in publications from 2017 to 2022, which highlights a growing interest and expanding research activity in the field, indicating its potential significance and relevance in the contemporary academic landscape.

翻译：本研究采用多维度综合科学计量方法，通过运用超越传统系统性文献综述的定量技术，分析2010年至2022年间多个搜索引擎与数据库来源的出版物记录，探究人工智能与机器学习在金融研究领域的知识基础。通过考察文章、期刊、作者、机构及国家的发表模式，本研究识别了具有影响力的著作、开创性贡献、思想领袖、主题集群、研究脉络及新兴研究前沿，最终深化了对人工智能与机器学习金融研究知识结构的理解。研究发现，2017年至2022年间出版物数量显著增长，凸显了该领域日益增长的学术关注度与研究活动的扩展态势，昭示其在当代学术格局中的潜在重要性与现实关联性。