What Distinguishes a Robo-Advisor?
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. o.V. 2020), while the investment volume has increased more than tenfold from around 756 million euros to 8.068 billion euros (cf. o.V. 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, 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.
In my last post, I detailed the customer advisory processes for a human advisor and a robo-advisor. Today I explain what causes these differences. This step is important to be able to understand and explain the differences between the two advisory processes and then, in a next step, to be able to derive the impact of robo-advisory services and to evaluate the advantages and disadvantages of robo-advisors compared to the traditional customer advisory process.
Process Characteristics of a Robo-Advisor
The advisory process of a robo-advisor has seven overarching characteristics: Accessibility, availability, standardization, automation, transparency, usability and efficiency.
|Overarching process characteristics||Individual process characteristics|
|Accessibility||Online accessibility |
Low minimum requirements
|Availability||Permanent availability |
|Standardization||Avoidance of subjective consulting errors|
|Usability||Uncomplicated onboarding / simplicity|
|Efficiency||Resource saving |
Efficiency in risk analyses
Efficiency in compliance
Some of the characteristics receive more attention in the literature than others. For example, the efficiency and usability of robo-advisors are referenced almost three times more, with 22 references each, than the accessibility of the service, with only eight references. The second most referenced overarching process characteristic, transparency, follows at a significant distance with 13 hits. In the following, I explain the overarching process characteristics and their individual components in more detail:
The advisory process of a robo-advisor takes place exclusively digitally via an internet website or smartphone application (cf. o.V. 2016d, 12, 15). In contrast to traditional investment advice, which usually requires a minimum investment amount of 100,000 euros (cf. Bloch/Vins 2017, 118), the customer target group is not restricted in digital asset management and no minimum investment volume is required. Furthermore, the service fees of a robo-advisor are significantly lower than those of traditional investment advice. For these reasons, accessibility, i.e. “easy” access to use the service, is characteristic for robo-advisors. In principle, this enables low-wealth clients to make use of the service of an asset management company (cf. Warmund/Lewis 2016, 5, Pertlwieser/Lehr 2017, 140).
Another process characteristic is the permanent availability of the robo-advisor: The advisory process of a robo-advisor can basically be started at any time and at any place, provided that the user has an internet-capable terminal device and the website of the robo-advisor is not subject to server problems (cf. o.V. 2017, 3, Fisch et al. 2017, 14f). In addition, a robo-advisor is able to permanently monitor customer portfolios that have already been allocated, automatically carry out rebalancing if necessary and inform the respective customer without delay. By using a robo-advisor, the customer’s investments can be permanently monitored, which would not be possible with a human advisor (cf. Bloch/Vins 2017, 118). Due to the fact that the customer can start the advisory process independently in terms of time and space, there are no appointments or opening hours to be considered. In the event of a server failure, however, no consulting service can take place and there is no customer acquisition.
The next process-related characteristic to be mentioned is standardization. The entire consulting process of a robo-advisor is standardized and therefore follows a fixed pattern. All process steps are unchangeable in their sequence and the processes they contain. For this reason, constant quality can always be guaranteed to every customer and human, subjective advisory errors can be excluded (cf. Siegismund 2018, 20, o.V. 2017, 4). Due to model portfolios and a fixed service fee, the robo-advisor, in contrast to the human advisor, is furthermore not subject to potential conflicts of interest due to sales commissions (cf. Bloch/Vins 2017, 119). However, standardization can also negatively influence the advisory process: Robo-advisors are programmed to provide predefined information within the questionnaire and do not take individual preferences into account or fail to deal with extraordinary situations that may arise within the advisory process. For example, the client has to decide on one of the suggested investment objectives, even if none of them matches his or her expectations. Also, comprehension problems on the part of the client may not be resolved. These scenarios can lead to investors with dissimilar investment intentions nevertheless being proposed the same and thus possibly unsuitable model portfolio or the advisory process having to be aborted (cf. (Kaya 2017, 3, Fisch et al. 2017, 17f, Singh/Kaur 2017, 41).
A fourth process characteristic of a robo-advisor is automation. For example, customer communication is automatically documented and emails are sent to the customer in case of market changes (cf. o.V. 2016a, 5, Kaya 2017, 3). Automated sub-processes allow the consulting process to be executed faster and without human intervention. This means that there are no personnel bottlenecks in investment consulting and workforce, time and costs can be reduced.
Another special feature of robo-advisors is their transparency, through which every step within the end-to-end process can be rationally justified (cf. Pertlwieser/Lehr 2017, 140, Warmund/Lewis 2016, 5, Hölscher/Nelde 2018, 70). Permanent access to the platform gives customers an overview of their investments and insight into their contract documents at any time (cf. Pertlwieser/Lehr 2017, 131, Siegismund 2018, 21). Before starting an advisory process, it is possible for customers to compare different robo-advisors and read experience reports (cf. Bloch/Vins 2017, 119, o.V. 2017, 3). This leads to the fact that clients can obtain sufficient information about offers and select the best provider for them. This reduces the risk of customers abandoning the consultation process.
A robo-advisor is also characterized by its usability: easy-to-understand questions and simple explanations of complex topics are part of the advisory process. Content is presented in a modern way (cf. Pertlwieser/Lehr 2017, 131) and the customer is actively involved in the process, in which, for example, he or she has to make investment decisions in a playful way based on sample situations and is thus involved in determining his or her attitude to risk (cf. Petry 2017, 29). The entire advisory process is carried out via a smartphone application or a website, which further enhances the customer experience (see Warmund/Lewis 2016, 5). The customer can sell investments already made or extend the monthly savings rate flexibly and without additional costs with just a few mouse clicks. Even if the entire portfolio is sold, it is possible to continue the robo-advisor account at a later date without incurring further costs (cf. Bloch/Vins 2017, 119). Since all process steps are digital, robo-advisors completely eliminate the use of paper and branch visits (cf. Bloch/Vins 2017, 118, Fisch et al. 2017, 14f). However, personal contact is not available due to the loss of the human advisor throughout the advisory process, which means that individual questions from the customer cannot be answered (cf. Singh/Kaur 2017, 41). Furthermore, it is not apparent how seriously and conscientiously a customer answers questions. For example, when impatient or lacking concentration, people tend to provide insufficient or random information, possibly leading to an inappropriate portfolio allocation (cf. Kaya 2017, 3).
The seventh and last process characteristic identified is efficiency. Due to this characteristic, the robo-advisor executes the individual sub-processes or activities within the advisory process in less time (cf. Stolberg 2017, 233). For example, algorithms can calculate the customer’s risk tolerance or determine new investment proposals in real time (cf. o.V. 2017, 6, Fisch et al. 2018, 26). Moreover, customer communication is exclusively digital and the advisory process is completed within ten to 20 minutes (cf. D’Acunto et al. 2019, 1989, Siegismund 2018, 21). The use of a robo-advisor is much more efficient and productive compared to the traditional advisory process, as the working speed of a human advisor is always lower than that of a robo-advisor (cf. Stolberg 2017, 233, o.V. 2016d, 12). As a result, both client and advisor have more time available for other things (cf. Kaya 2017, 3). The efficiency can be illustrated in particular by the fact that only one account manager per 20,000 customer accounts is needed for account management with a robo-advisor, while up to three advisors serve 60 to 150 customers in traditional asset management.
Based on the process characteristics of a robo-advisor, we can already see some improvements, but also deteriorations of the advisory process compared to traditional investment advice. How exactly the characteristics influence the advisory process, I will visualize next week using the graphic of the traditional advisory process. Finally, I will try to answer the question which form of advisory is the better one and in what direction investment advisory will develop further.
|[Alt/Puschmann 2016]||Alt, R., Puschmann, T., Digitalisierung der Finanzindustrie, Springer-Verlag, Berlin, 2016.|
|[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.|
|[D’Acunto et al. 2019]||D’Acunto, F., Prabhala, N., Rossi, A.G., The Promises and Pitfalls of Robo-Advising, in: The Review of Financial Studies, 32 (2019) 5, S. 1983–2020.|
|[Dapp 2016]||Dapp, T.-F., Robo Advice, Deutsche Bank Research, 2016.|
|[Fisch et al. 2017]||Fisch, J.E., Labouré, M., Turner, J.A., The Economics of Complex Decision Making: The Emergence of the Robo Adviser, University of Pennsylvania Law School (Institute for Law and Economics)/Harvard University/Pension Policy Center, 2017.|
|[Fisch et al. 2018]||Fisch, J.E., Labouré, M., Turner, J.A., The Emergence of the Robo-advisor, University of Pennsylvania Law School (Institute for Law and Economics)/Harvard University/Pension Policy Center, 2018.|
|[Kaya 2017]||Kaya, O., Robo-advice – a true innovation in asset management, Deutsche Bank Research, Frankfurt am Main, 2017.|
|[o.V. 2016a]||o.V., The future of advisory: exploring the impact of robo on wealth management, Finextra Research, 2016.|
|[o.V. 2016b]||o.V., Cost-Income Ratios and Robo-Advisory Why Wealth Managers Need to Engage with Robo-Advisors, Deloitte GmbH Wirtschaftsprüfungsgesellschaft, 2016.|
|[o.V. 2017]||o.V., Positionspapier des Bankenverbandes zu Robo-Advice, Bundesverband deutscher Banken e.V., Berlin, 2017.|
|[o.V. 2020]||o.V., Robo-Advisors, Statista, 2020.|
|[Pertlwieser/Lehr 2017]||Pertlwieser, M., Lehr, J. von der, Robo Advisors, in: Fleischer, K., Trends im Private Banking, 3. Aufl., Bank-Verlag GmbH, Köln, 2017, S. 129–145.|
|[Petry 2017]||Petry, M., Robo Advisor: Einfache Lösungen sind gefragt, in: bank und markt (2017) 4, S. 28–30.|
|[Siegismund 2018]||Siegismund, B., Rolle rückwärts – Digitale Vermögensverwalter mit persönlicher Beratung, in: bank und markt (2018) 12, S. 19–21.|
|[Singh/Kaur 2017]||Singh, I., Kaur, N., Wealth Management through Robo Advisory, International Journal of Research – Granthaalayah, 2017.|
|[Stolberg 2017]||Stolberg, M., Finanzdienstleister investieren in künstliche Intelligenz, in: Finanzierung Leasing Factoring (2017), S. 232–235.|
|[Warmund/Lewis 2016]||Warmund, J., Lewis, B., Robo advising: Catching up and getting ahead, KPMG AG Wirtschaftsprüfungsgesellschaft, 2016.|