Abstract Fueled by rapid advances in the contemporary era, the scope and application of artificial intelligence (AI) have expanded across sectors, including finance. This study aims to examine taxonomy, themes, and research clusters within AI research in finance through bibliometric analysis and a systematic literature review. The study analyzed 429 articles published between 2010 and 2025 in top finance journals indexed in the Scopus database. Descriptive analysis was performed using RStudio and VOS viewers to identify publication and citation trends, the most contributing countries, and co-authorship patterns. A systematic literature review synthesized the existing literature, consolidating the initial 19 clusters into five main themes. The analysis revealed five main themes in AI research in finance: technological, conventional, behavioral, regional, and consequences. The findings provide practical implications for corporate stakeholders in the finance industry, highlighting AI’s role in informed decision-making. This study offers a comprehensive overview of AI’s diverse applications in finance, mapping the evolution of research themes and clusters. It provides valuable insights for practitioners and researchers, emphasizing the need for future research on recent advancements in disruptive technologies and their transformative impact on the financial landscape. Future research should focus on recent advancements in disruptive technologies and their transformative impact on the financial landscape. Continued exploration of AI’s evolving role in finance will be crucial for understanding its full potential and limitations.
Rasheed et al. (Mon,) studied this question.