Artificial Intelligence in Financial Decision-Making: A Bibliometric Analysis of Research Trends, Intellectual Structure and Emerging Themes

Abstract

Artificial intelligence (AI) has become a defining force in financial decision-making, reshaping how individuals, institutions, and markets evaluate risk, allocate capital, and detect fraud. As scholarly output on this intersection has grown rapidly over the past decade, the literature has become fragmented across databases, time windows, and disciplinary lenses, making it difficult to discern a unified picture of the field's development. This paper addresses that gap through a structured meta-bibliometric synthesis that consolidates findings from eight peer-reviewed bibliometric studies of AI in finance and financial decision-making published between 2021 and 2026, collectively drawing on Scopus- and Web of Science-indexed corpora ranging from 202 to over 1,000 documents and spanning 1987 to 2025. Using a systematic search-and-synthesis protocol and cross-study thematic content analysis, the study maps four dimensions of the field: publication growth trajectories, leading contributors, the intellectual structure of thematic clusters, and emerging research frontiers. Findings show accelerating publication growth since 2017 and a further surge after 2022, United States-, China-, and United Kingdom-led contribution patterns, and six recurring thematic clusters spanning algorithmic trading, credit risk and fraud detection, robo-advisory and investor behavior, explainable and ethical AI, ESG-linked finance, and financial inclusion. The study offers researchers, practitioners, and policymakers a consolidated map of the field's intellectual structure and proposes a future research agenda centred on explainability, regulatory alignment, and generative-AI adoption in financial decision contexts.