Financial Robo-Advisor: Learning from Academic Literature
Abstract
Financial Robo-Advisor is the technology that integrates machine learning and self-identification to determine investment decisions. This study explores the financial robo-advisor based on bibliometric analysis and a systematic literature review. The method used three steps: determining the keyword, bibliometric analysis of literature metadata using VOSviewer, then collecting and analysing the articles. The bibliometric analysis results show five cluster keywords defined with different colors. In the network visualization, the robo-advisor connects to other keywords: investment, fintech, and artificial intelligence. Furthermore, the systematic literature review shows that the articles are divided into seven research objectives: (1) Law, Regulation, and Policy; (2) Investment Literate and Education; (3) Offered Services; (4) Present Risk-Portfolio Matching Technology; (5) Optimal Portfolio Methods; (6) Human-Robo Interaction; (7) Theoretical Design and Gap. Furthermore, this study can be used by academicians and practitioners to find out about robo-advisors based on an academic perspective.
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