Abstract
BACKGROUND: This bibliometric study examines the intersection of artificial intelligence (AI) and finance, providing a comprehensive analysis of its evolution, central themes, and avenues for further exploration. The study aims to uncover the theoretical foundations, methodological approaches, and practical implications of AI in financial contexts. METHODS: The research employs bibliometric techniques, using 607 Web of Science (WoS) indexed papers. The Theory-Context-Characteristics-Methodology (TCCM) framework guides the analysis, focusing on thematic mapping to explore key topics. Core areas such as risk management, market efficiency, and innovation are analyzed, alongside emerging themes like ethical AI, finance applications, and factors influencing AI-driven financial decision-making. RESULTS: The findings reveal critical gaps in interdisciplinary methods, ethical considerations, and methodological advancements necessary to develop robust and transparent AI systems. Thematic mapping highlights the increasing importance of ethical AI practices and the influence of AI on financial decision-making processes. Emerging research areas emphasize the need for innovative frameworks and solutions to address current challenges. CONCLUSIONS: This study provides valuable insights for academics, industry practitioners, and policymakers to harness transformative potential of AI in finance. This research offers a foundation for future studies and practical applications by addressing key gaps and promoting interdisciplinary and ethical approaches in a rapidly evolving field.