Искусственный интеллект в финансах: Наукометрический обзор

In recent years, the adoption of artificial intelligence in finance has surged, leading to a proliferation of AI models and research publications. Previous studies utilize bibliometric analysis to explore the thematic structures of literature in this area, assessing scholarly performance and offering insights into the development trajectories of AI Finance. However, these studies do not fully capture recent developments due to the rapid increase in publications and the emergence of novel AI models and analysis tools. This study addresses this gap by employing scientometric and content analysis techniques to conduct a detailed retrospective review of AI Finance literature from 1990 to 2024. Through citation analysis, clustering, thematic mapping and bibliographic coupling, the study identifies shifts in publication trends and research focus, key contributors, and novel clusters and classifications of AI Finance research. The studies also examines the regulation of AI in finance and identifies key issues related to AI regulation. Several gaps in AI finance and regulation literature were found and discussed. The findings provide valuable insights for researchers, finance professionals, and policymakers seeking to understand and navigate the landscape of AI in finance and its regulatory environment.