The Rise of Robo-advisors in Wealth Management: Their Capabilities and Limitations

In recent years, it has become increasingly more popular for financial institutions to deploy Robo-advisors for assisting their clients to manage assets or giving financial investment advices of various kinds. However, there are very few research articles about Robo-advisors from among the published literature. Objective: The main contribution of this article is to fill the aforementioned research gap by collecting relevant information of Robo-advisors from both existing literature and industrial news and then conduct a systematic review of the stateof- the-art on Robo-advisors. Our findings are that the core functionalities of existing Roboadvisors include financial budgeting, general financial planning, retirement planning, automated portfolio management and optimization, portfolio sharing via social media, financial investment education, and so on. However, the functionalities of existing Roboadvisors are still considered primitive. In particular, they lack of advanced reasoning capabilities, weak in personalization, and seldom work collaboratively in the form of a team of intelligent advisors. Our critical review of the state-of-the-art on Robo-advisors can provide both researchers and practitioners with better technological insights so that they can focus on the relevant areas for the further advancement and improvement of truly intelligent Robo-advisors in coming years. The design and development of Robo-advisors is still at the infant stage. Future research work should further develop the advanced reasoning capabilities of Robo-advisors such as common sense reasoning and emotion-aware reasoning. Moreover, personalization is an essential element of truly intelligent Robo-advisors. To alleviate the aforementioned shortcomings of existing Robo-advisors, we propose a novel model of Roboadvisors. In particular, the proposed next generation architecture of Robo-advisors, which comprise a emotion detector, advanced reasoned, personalization engine, asset optimizer, dynamic information extractor, and a user profile are just able to alleviate the shortcomings of existing Robo-advisors.