Transformational application of Artificial Intelligence and Machine learning in Financial Technologies and Financial services: A bibliometric review
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914731417141248 |
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| author | Kanaparthi, Vijaya |
| author_facet | Kanaparthi, Vijaya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15710 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformational application of Artificial Intelligence and Machine learning in Financial Technologies and Financial services: A bibliometric review Kanaparthi, Vijaya Computational Engineering, Finance, and Science 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. |
| title | Transformational application of Artificial Intelligence and Machine learning in Financial Technologies and Financial services: A bibliometric review |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2401.15710 |