An interdisciplinary bibliometric dive into robo advisory: mapping the intellectual landscape and future research agenda

Purpose The purpose of this study is to present state-of-the-art research on algorithms to leverage Robo-advisors by reviewing the key financial theories and techniques behind popular Robo-advisors, to review the role of explainable artificial intelligence for investment strategy using a Robo advisor, is to explore the publications on Robo-advisory developed over the last few years and to present current and future trends of asset wealth management using Robo-advisors. Design/methodology/approach In this study, a bibliometric review methodology has been employed to examine classical machine learning and deep learning algorithms, as well as financial theories, and propose how a Robo-advisor utilising a combination of machine learning algorithms, financial theories, and natural language processing can provide more effective and cost-efficient solutions, thereby attracting more customers. Findings This bibliometric analysis mapped the intellectual landscape and future research agenda for Robo-advisory by identifying the finance theories needed to enhance the service level offered to customers by the investment sector, based on descriptive, thematic and coupling analyses. Originality/value This bibliometric analysis is the first of its kind to bring an interdisciplinary perspective from the fields of finance, AI in specific and computer science in general. Moreover, this study contributes by a timely bibliometric analysis of Robo-advisory to help scholars identify current trends and recommend future research using classical portfolio-balancing theories, e.g. modern portfolio theory, and related techniques, e.g. constant rebalancing.