Application of recommendation systems in the development of Robo Advisors: A Bibliometrics Method

Recognizing customers` requests and offering them personal investment suggestions is an essential aspect of a useful and effective consulting strategy. Many households trust financial advisors for investment guidance. Intelligent data analysis is one of the fields of artificial intelligence that solves the problem of learning automated systems without an explicit program. Financial companies have found that they need to adapt quickly to the environment and use automated systems to save money on the cost and accuracy of financial advice to investors. In recent years, a type of technology-based counseling has been introduced as an alternative to Robo Advisors. Robo Advisors is a financial advisor who can assist through machine learning algorithms to automatically analyze the financial product risk level and provide portfolio advice. Robo Advisors are digital platforms that offer algorithm-based and automated financial planning services such as investing. In this study, a systematic review of the empirical studies done on Robo Advisors is given and at the end, a proposed framework for designing Robo Advisors is presented.