  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.
