What Is a Robo-Advisor and Why Should We Care?
Online asset management has been experiencing a rapid rise in Germany for several years. Since 2017, the number of users has grown by a factor of 7 from around 291,000 in 2017 to around 2.01 million in 2020 (cf. Statista 2020), while the investment volume has increased more than tenfold from around 756 million euros to 8.068 billion euros (cf. Statista 2020). Two factors in particular are key to this trend: firstly, the loss of trust in personal banking advisory services caused by the financial crisis in 2007, and secondly, the increasing demand for digital offerings by digital natives. The new generation of customers who have grown up with smartphones and tablets, also known as “Generation Y,” is much more attuned to electronic communication, which means that personal contact such as with customer advisors at banks is losing relevance (cf. Alt/Puschmann 2016, 29). In the course of the shift from a personal, individual customer experience at a bank to the desire for standardized and digitized processes, “robo-advisors”, which replace personal, human advice with the offer of algorithm-based investment proposals, are becoming increasingly important (cf. Dapp 2016, 1).
For this reason, in this series of articles over the next few weeks, I would like to provide an overview of what a robo-advisor is, what business models and strategies robo-advisors are pursuing, and how the traditional customer advisory process is changing through the use of robo-advisors. The articles are based on my bachelor thesis “An Analysis of the Impact of Robo-Advisors on the Customer Advisory Process in the Investment Sector”, which I wrote at the Information Systems Institute of the Faculty of Economics at Leipzig University. Today’s article is about how robo advisory services are defined and how they came into being, whether they are serious competition for banks, and what stage of development robo-advisors are at now. Let’s now move on to the title question:
What Is a Robo-Advisor?
This question is actually not that easy to answer. One of the reasons is that the scope of the services offered and the business models of different robo-advisors differ greatly from one another (see [Hölscher/Nelde 2018, 68]). But leaving that aside, the term alone is already controversial, firstly because robo-advisors in most cases have nothing at all to do with artificial intelligence, i.e. with robots, and secondly just as little to do with traditional advice, i.e. an individual dialog between an advisor and a potential investor. Interestingly, the term is also mainly used in the financial industry, even though the idea of robot-assisted advice could in principle be applied to many industries (cf. [Bahlinger/Reichert 2018, 1f]).
Leaving these considerations aside for a moment and looking at the definition of the Association of German Banks, robo-advisors are “[…] algorithm-based recommendation management for investment advice.” [Bundesverband deutscher Banken e.V. 2017, 2], in reality mostly in the form of an online platform. The core of robo advisory services is to replicate the traditional customer advisory process for an investment, guided by a human advisor, as best as possible using algorithms and without the intervention of such an advisor (cf. [Hölscher/Nelde 2018, 68]). However, this is an ideal conception; most solutions currently involve so-called “risk profiling” by which the risk propensity and risk-bearing capacity of customers are determined in order to be able to offer a suitable portfolio. In most cases, the providers of robo-advisors even dispense with such a determination of risk and merely inquire about the customer’s risk appetite (cf. [Bahlinger 2018, 28]).
Robo-Advisors – the Evolution of Serious Competition?
The origin of the robo-advisor business model goes back to 2008, when the US fintechs Wealthfront and Betterment were founded as the first robo-advisors (see [Hölscher/Nelde 2018, 68]). The term “fintech” is the abbreviation of “financial technology” and describes, on the one hand, innovative IT financial solutions and, on the other hand, the very companies that offer those solutions (cf. [Puschmann 2017, 70]). Fintechs can be divided into six categories, which in turn contain different business models, as can be seen in Figure 1.
The robo-advisor business model belongs to the field of investment fintech. A fintech with a focus on robo advisory services can alternatively also be described as a “banktech” (cf. [Alt et al. 2018, 239]). Until 2014, the term “fintech” was still rather unknown, especially in Germany. This changed rapidly when, among others, the first robo-advisors named “vaamo”, now “moneyfarm”, and “quirion” were founded over here in the said year (cf. [Alt et al. 2018, 235], [Bloch/Vins 2017, 117]). Today (as of December 2020), Germany is one of the world’s largest markets with its 30 robo-advisors (cf. [Franke 2020]).
The decisive factor for the emergence of the robot-assisted business model was the loss of trust in the banking industry mentioned at the beginning, but also the now existing technological feasibility. Above all, however, people saw the opportunity in providing access to the capital market to a much broader audience through low fees (see [Ernst & Young 2018, 3]). One potential customer group, for example, was the low-income younger generation, most of whom were unable to take advantage of cost-intensive and traditional investment advice. This customer group, which had been neglected by the banks, was to be served by robo-advisors (see [Sironi 2016, 25]). Accordingly, to date (as of December 2020), there is the possibility to invest in the capital market starting at a minimum investment or monthly savings rate of one euro (cf. [Hölscher/Nelde 2018, 69], [Franke 2020]).
Initially, robo-advisors or the young fintechs were not considered serious competition by traditional asset managers and banks (cf. [Puchalla 2017, 98]). According to a survey by the Institute for Asset Management at Aschaffenburg University of Applied Sciences (cf. [Institut für Vermögensverwaltung (Hochschule Aschaffenburg) 2016, 34]), only six to seven percent of all 134 asset managers surveyed felt threatened by robo-advisors in 2016. This impression changed within a few years with the rapid growth of robo-advisors. As a result, there is now an increasing number of cooperations, as banks and asset managers gain access to new customer groups and robo-advisors receive support in transactions, regulation and the scaling of their business models in return (see [Puchalla 2017, 98]). Three forms of cooperation are in use:
- the “white label solution”
- the “platform solution” and
- the “software-as-a-service solution”.
The simplest and most straightforward variant is the “white-label solution”, in which the cooperating robo-advisor merely offers its complete service in the company’s design and under its name. The danger of this method for the bank is a possible migration of customers to the robo-advisor. In the two other forms of cooperation, the robo-advisor acts purely as an IT service provider that digitally implements the bank’s products and processes. Thus, the bank’s investment advisor remains available to customers for queries in these two solutions. The only difference is that in the platform solution, the robo-advisor can be assigned settlement and custody account management, whereas in the software-as-a-service solution, all non-technical tasks remain with the bank (see [Klein 2017, 19]).
On the Way to an Intelligent, Robotic Investment Advisor
Since the emergence of the new business model, robo advisors have steadily increased in complexity. Figure 2 below shows the four stages of evolution:
Each evolutionary stage is complemented by the characteristics of the previous one. A first-generation robo-advisor is a kind of tool that allows prospective investors to view product or allocation proposals based on their answers in an online questionnaire. The robo-advisor selects the investment products from a predefined list. Accordingly, the customer is responsible for purchasing and managing the investments himself. The second generation of robo-advisors has an investment platform through which the customer can invest directly in prefabricated investment funds with the help of an account provided. Asset managers execute the buy orders, create the asset allocations and monitor the entire process. The third evolutionary stage of robo-advisors, where the majority of all providers are today (as of December 2020), is based on algorithms that suggest investment decisions and portfolio adjustments according to a strategy that has already been defined in advance. In this type of robo-advisor, the process is predominantly automated, with an investment manager merely performing a final review. Finally, Robo-Advisors 4.0 offer the maximum in automation with integrated artificial intelligence and thus self-learning algorithms that are able to make independent purchases and sales in real time in response to market fluctuations or the individual interest of the investor. Such a form of robo-advisor does not yet seem to exist (see [Deloitte 2016, 2f]).
Next week, I will explain which business models and strategies robo-advisors are using in Germany in the second post of my series on robo advisory services. So stop by next week or register to make sure you don’t miss any of the posts.
|[Alt et al. 2018]||Alt, R., Beck, R., Smits, M.T., FinTech and the transformation of the financial industry, in: Electronic Markets, 28 (2018) 3, S. 235–243.|
|[Alt/Puschmann 2016]||Alt, R., Puschmann, T., Digitalisierung der Finanzindustrie, Springer-Verlag, Berlin, 2016.|
|[Bahlinger/Reichert 2018]||Bahlinger, T., Reichert, S., Digitale Beratung – Verfügbarkeit intelligenter Software für die Finanzberatung – Auf der Suche nach Künstlicher Intelligenz bei Fintechs, Technische Hochschule Nürnberg, 2018.|
|[Bloch/Vins 2017]||Bloch, T., Vins, O., Private Banking via FinTech: Strategie und Schnittstellen, in: Fleischer, K., Trends im Private Banking, 3. Aufl., Bank-Verlag GmbH, Köln, 2017, S. 111–128.|
|[Bundesverband deutscher Banken e.V. 2017]||Bundesverband deutscher Banken e.V., Positionspapier des Bankenverbandes zu Robo-Advice, Berlin, 2017.|
|[Dapp 2016]||Dapp, T.-F., Robo Advice, Deutsche Bank Research, 2016.|
|[Deloitte 2016]||Deloitte, The expansion of Robo-Advisory in Wealth Management, Deloitte GmbH Wirtschaftsprüfungsgesellschaft, 2016.|
|[Ernst & Young 2018]||Ernst & Young, The evolution of Robo-advisors and Advisor 2.0 model, 2018.|
|[Franke 2020]||Franke, D., Robo-Advisor – Vergleich und Erfahrungen 2020, Franke-Media.net, 2020, URL: https://www.brokervergleich.de/robo-advisor/, accessed 16.12.2020.|
|[Hölscher/Nelde 2018]||Hölscher, R., Nelde, M., Darstellung, Funktion und Portfolioaufteilung von Robo-Advisory, in: Zeitschrift für das gesamte Kreditwesen (2018) 2, S. 68–73.|
|[Institut für Vermögensverwaltung (Hochschule Aschaffenburg) 2016]||Institut für Vermögensverwaltung (Hochschule Aschaffenburg), Ergebnisse der dritten Befragung, 2016.|
|[Klein 2017]||Klein, G., Robo Advisor: Kooperation statt Konkurrenz, in: bank und markt (2017), S. 18–20.|
|[Puchalla 2017]||Puchalla, G., Finanztechnologie 2.0: Vom Kooperationspartner zum Trusted Advisor, in: Fleischer, K., Trends im Private Banking, 3. Aufl., Bank-Verlag GmbH, Köln, 2017, S. 95–110.|
|[Puschmann 2017]||Puschmann, T., Fintech, in: Business & Information Systems Engineering, 59 (2017) 1, S. 69–76.|
|[Sironi 2016]||Sironi, P., FinTech Innovation, John Wiley & Sons, Chichester, 2016.|
|[Statista 2020]||Statista, Robo-Advisors, 2020.|