Hybrid models – the next trend in the RA sector?

Bill Gates stated that “Banking is necessary, Banks are not” (Jung et al., 2018, p. 81). And indeed, the digitalization (blockchain & AI) of the financial world is expected to grow exponentially––e.g., the DeFi movement in the crypto sphere. However, despite such competitors, robo-advisors (RAs) “are perhaps the most important disruptive trend in wealth and asset management today” (Beketov et al., 2018, p. 363). The value of assets under management by robo-advisors is expected to more than triple between 2018 and 2023 (Abraham et al., 2019, p. 2). Typically, RAs use automated algorithms to make recommendations on how to allocate funds across different types of assets. In most cases, these algorithms “optimal portfolio is one that maximizes the expected return given a level of risk tolerance or, alternatively, minimizes risk given a level of expected return” (Abraham et al., 2019, p. 2). Furthermore, the robo-advisory business model is easily scalable, rendering the service an interesting business model from the service provider’s perspective (Jung et al., 2018, p. 83). However, customers still prefer hybrid solutions, allowing them to search for information and compare available products online, “but still request human advisory before committing to an investment” (Jung et al., 2018, p. 83). A Morgan Stanley note said, the “financial sector consumer often needs some sort of human contact, especially when abrupt market moves lead to unexpected losses.” Regulators also found that some firms did not match clients’ knowledge with an appropriate investment strategy. Consequently, the rise of RAs requires consumers to understand the limitations of these services, and to get proper financial education. Policy makers need to grapple with regulatory and supervisory practices. However, many previous regulations have been found to increase the wealth gap, while excluding the small investor. Based on such an assumption, RAs may in fact be a way to level the playing field, but the question remains whether, “in the event of a 20% drop in the market, robo-customers would pull out their money, irrespective of their long-term goals”. Hence, the success of RAs may depend on the ability to sufficiently assess (Tertilt & Scholz, 2018), and  educate their customers: “Given the missing human advisor, we believe the ability to individualize will be a distinguishing feature among robo-advisors” (Faloon & Scherer, 2017). Furthermore, as RAs may be more attractive to the small investor, the key question for the growth of the entire RA sector is whether and how wealthy investors will invest in RAs (Beketov et al., 2018, p. 369). More wealthy investors also prefer some human interaction, and hybrid models are now frequently considered to be the next trend in the RA sector (ibid.).

The financial industry is continuously adopting new technologies to deliver financial services in cheaper and more efficient ways (Uhl & Rohner, 2018). In 2015, BlackRock, the world’s largest asset manager, acquired the robo-advisory company Future Advisor. However, as customers seem to prefer some level of human interaction, and there is also a need to ensure sufficient knowledge amongst investors, a combination of traditional banking and RAs may provide an opportunity to target the mass of less-wealthy customers, but also to generate additional revenue through separate fees (Jung et al., 2018, p. 83). It is expected that traditional wealth managers will respond by providing new and improved customized and integrated services at competitive fees (Phoon & Koh, 2017), and RAs will also be in competition with social trading platforms, such as eToro.


Abraham, F., Schmukler, S. L., & Tessada, J. (2019). Robo-advisors: Investing through machines. World Bank Research and Policy Briefs(134881).

Beketov, M., Lehmann, K., & Wittke, M. (2018). Robo Advisors: quantitative methods inside the robots. Journal of Asset Management, 19(6), 363-370.

Faloon, M., & Scherer, B. (2017). Individualization of robo-advice. The Journal of Wealth Management, 20(1), 30-36.

Jung, D., Dorner, V., Glaser, F., & Morana, S. (2018). Robo-advisory. Business & Information Systems Engineering, 60(1), 81-86.

Phoon, K., & Koh, F. (2017). Robo-advisors and wealth management. The Journal of Alternative Investments, 20(3), 79-94.

Tertilt, M., & Scholz, P. (2018). To advise, or not to advise—How robo-advisors evaluate the risk preferences of private investors. The Journal of Wealth Management, 21(2), 70-84.

Uhl, M. W., & Rohner, P. (2018). Robo-advisors versus traditional investment advisors: An unequal game. The Journal of Wealth Management, 21(1), 44-50.

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