Artificial intelligence in finance studies: Bibliometric approach to literature indexed in Scopus

Abstract

Objective. This study aims to analyze the scientific production indexed in the Scopus database on the application of artificial intelligence (AI) to finance studies between 2007 and 2023. Design/Methodology/Approach. The study design is non-experimental (transectional) and quantitative (descriptive). The most representative authors, the documentary typology that supports the results, and the principal publications were identified and analyzed. General citation indicators were calculated to ascertain the scientific impact associated with the topic. Spectral maps of country and word density were prepared to determine the main characteristics concerning these bibliographic variables. Results/Discussion. Notwithstanding the extensive temporal scope of the study, the application of AI to finance has not been evidenced in the extant literature until 2017. No significant contributors or highly influential journals are identified; studies are sporadic and consistent with the topic’s novelty. Neverthe-less, this subject has a high scientific impact, with an average of 20 citations per paper. Conclusions. The application of AI in finance is a relatively recent phenomenon. The countries of Asia and India are at the forefront of scientific production, as evidenced by Scopus’s data analysis. The works analyzed exhibit a high density of terminology and a plethora of journals in the computational field that publish on this topic. Furthermore, publication practices manifest in the form of event papers, which are published at a similar rate to scientific articles. Originality/Value. The value of this study lies in its originality, which stems from an in-depth examination of existing literature on these topics in Scopus. This approach enables a comprehensive bibliometric analysis, informing future research in this field.