Robo-Advisors in Wealth Management: A Bibliometric Study of Research Evolution

Abstract

Robo-advisors have emerged as a transformative force in wealth management, leveraging artificial intelligence (AI) and machine learning to provide automated financial advisory services. This study conducts a bibliometric analysis of research on robo-advisors using data exclusively from the Scopus database and analyzed through VOSviewer. The findings reveal that research in this field has evolved from foundational discussions on fintech and artificial intelligence to advanced themes such as machine learning, decentralized finance, and algorithmic transparency. The keyword analysis highlights "wealth management," "fintech," and "machine learning" as central themes, while the co-authorship network indicates strong interdisciplinary collaboration among researchers. Additionally, the study identifies key regulatory and ethical challenges, including data privacy, fiduciary responsibility, and algorithmic bias, which require further investigation. The discussion explores the technological advancements, investor behavior, and regulatory landscape shaping the future of robo-advisory services. This research contributes to the growing academic discourse by mapping the intellectual structure of robo-advisor studies and suggesting future research directions, particularly in the areas of explainable AI (XAI), blockchain integration, and personalized financial advisory models.