Digitalization of Wealth Management

How the growth of Robo Wealth Management solutions disrupts a closed industry

Read the article and learn about:

  • The current market situation and trends in digital Wealth Management
  • The various types of robo-advisors and their technological requirements
  • A case study about a recently implemented robo-advisor at a major Central European asset manager
Digitalization of wealth management
Stefan Schulmeister
Chief Investment Strategist, Erste Asset Management
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Digitalization of wealth management
Anders Kirkeby
Technical Fellow, Vice President, SimCorp
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Clemens
Clemens Donà
Business Consultant, SimCorp
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Throughout the 20th century, Wealth Management was a privilege for large wealth holders. This concentration meant that Wealth Management companies had a very narrow and homogenous client base. In recent years, a disruptive trend has emerged in the industry. Driven by technological innovation, new digital services have been launched, increasing pressure on the profit margins of the industry. There is strong evidence that wealth managers must incorporate this technical evolution into their product portfolios, not only to retain existing and increasingly cost-sensitive clients, but also to attract the large share of the population (non-High Net Worth Individuals) who, for the first time ever, are now also potential clients.

 

As opposed to the traditional, consulting-intensive Wealth Management that linked highly-skilled portfolio managers to wealthy clients, a robo-advisor is a digitalized Wealth Management service that is app or web accessible. The service is based on implemented algorithms designed by portfolio managers and automatically executed. Typically, a client’s onboarding process to a robo-advisor and the subsequent portfolio management follows the steps below:1

  1. Answer a questionnaire: A standardized questionnaire to assess the client’s investment horizon and risk profile. Additional information such as his or her personal investment framework and interest in ESG securities can be requested to later suggest more suitable products.
  2. Determine risk-return profile: The evaluation of the questionnaire will assign a risk profile to the client.
  3. Asset allocation suggestion: Based on the answers in step (1), the robo-advisor will suggest a suitable asset allocation. Normally, the portfolio managers have predefined a pool of strategies out of which the best fit for the client is proposed. The security selection will traditionally be based on ETFs or other passive products as the most inexpensive way to implement strategies.
  4. Rebalancing: Over time, the weights of the single securities in the client’s portfolio will shift away from the asset allocation due to changes in market values. Therefore, a regular rebalancing has to be performed to match the asset allocation again.

Current market trends

By the end of 2018, the global amount of assets under management (AuM) by robo-advisors was EUR 490 billion. Compared to 2016, this meant a growth rate of 107% year-on-year. Estimates through to 2023 project AuM to grow by 36% year-on-year, reaching EUR 2.3 trillion by 2023.

Digitalization of wealth management

Figure 1: Global assets under management by robo-advisors (forecast from 2020)

Digitalization of wealth management

Figure 1: Global assets under management by robo-advisors (forecast from 2020)

Another insight from conducted market research2 is the geographic market segmentation. As you can see in the graph below, the US market has the highest penetration rate, with China expected to grow strongest in the coming years. Even in the US, however, less than 2% of the population is using a robo-advisor service.

Digitalization of wealth management

Figure 2: Assets under Management by robo-advisors by region

Digitalization of wealth management

Figure 2: Assets under Management by robo-advisors by region

In terms of players in the market, Vanguard is by far the largest with EUR 127 billion AuM, followed by Schwab (EUR 37 billion AuM) and TD Ameritrade, Betterment and Wealthfront (all with EUR 10-20 billion AuM). The fact that those firms are all American showcases the predominant market position of this region. However, the catch-up potential in other markets is high. Surveys show that clients are prone to subscribe to digital Wealth Management tools offered by their principal bank3, suggesting that Asian and European wealth managers have a good chance to participate in the rapidly growing robo-advisor market.

Nevertheless, all that glitters is not gold. In recent times, a couple of well-known robo-advisors have gone out of business as they struggled to make a profit (UBS’ or Investec’s robo-advisor attempts are two good examples). While the individual reasons for the withdrawals differ, two insights from those casualties are:

  1. The value proposition of the digital service should be easy to understand: It may be the cheapest offer in the market or more exclusive with a clear explanation for the higher fees.
  2. A big customer base is indispensable due to low fees per customer. Both a sound technological solution and access to a wide pool of potential clients are key to success.

Finally, the current market situation is a stress test for both market leaders and newcomers in digital Wealth Management. In the past couple of years, most asset classes have experienced steady growth. However, the outbreak of the coronavirus pandemic and the massive decline of the price of oil represent an unprecedented challenge for robo-advisors. In times of high volatility in almost all asset classes and securities, rebalancing at the right time can limit losses. Therefore, the implemented logic regarding how and when precisely to rebalance portfolios will distinguish good from mediocre providers. It goes without saying that good providers have to offer technology that can reliably deal with increased activities in volatile markets.

Different stages of robo-advisors and technological prerequisites

The most basic version of robo-advisors available in the market are phone-based apps (and sometimes websites) with a questionnaire and a recommendation for a portfolio allocation. The scope consists literally of the advising part - the user needs to (1) conduct the initial purchase proposals in his or her own account and (2) to monitor it on an ongoing basis reacting to market value changes. Mostly, the suggested instrument types are exchange traded securities.

At the next level of robo-advisor services, rather than proposing certain products, the questionnaire is used to allocate the client to a portfolio set up by a professional manager. Such portfolios usually contain a wider range of instrument types (not necessarily exchange traded anymore) and are actively managed. The client is thus advised how to invest their money in the most suitable way and the investment is executed for them. While some automated limit rules might be in place, the ultimate trade decisions are taken by portfolio managers.

The decisive feature at the next level of robo-advisor services is the automation of investment executions. Investment professionals are responsible for the definition of appropriate algorithms that then will run the daily operations and decide if adjustments to the current portfolio holdings are required due to market fluctuations or changes in risk properties of the investor and/or the securities. Although a manager’s approval of the suggested trades might still be required in certain situations, in the target model of full automation, portfolio managers will spend more time on value creating activities such as assessing and improving the ability of the algorithms to generate market return.

The most advanced stage of robo-advisors sees algorithms also involved in the definition of investment rules. Based on the answers to the initial questionnaire an appropriate asset allocation is chosen and continuously evaluated by help of artificial intelligence.

Each kind of robo-advisor requires different technological prerequisites: For the first level, a user-interfacing web application suffices, while for the second level, connectivity to exchanges and market data providers is required, as well as some kind of book-keeping system. When the execution of investment decisions is handed over to algorithms, the requirements to the underlying system include a rebalancing engine and the conduction of limit checks. Finally, the determination of the optimal asset allocation by artificial intelligence requires access to real-time market data and financial databases, and the know-how to implement and understand the AI inside the organization.4 5 6

The technological expertise to develop a robo-advisor service seems to be a natural entry door for tech companies wanting to take a first step into the Wealth Management industry. As Apple launched a credit card and rumours about Facebook’s cryptocurrency continue, the authors consider a market entry of a tech company to the Wealth Management industry possible. While certainly being favoured at the development, whether a market entry of an unprecedented competitor would disrupt the industry remains to be seen. Incumbents can prepare for potential disruptions by offering a solid technological solution paired with outstanding expertise of their portfolio managers.   

Robo-advisor implementation at Erste Asset Management

Erste Asset Management (EAM) is the asset management arm of Erste Group Bank AG, one of the largest financial service providers in Central and Eastern Europe, measured in balance sheet size.7 The main motivation for EAM to develop the Invest Manager, their robo-advisor offering, lies in the creation of a digital retail sales channel through which potential clients are addressed. The option to customize the investment strategy within suitable bandwidths helps to advertise this solution as dynamic and flexible rather than simply a static investment plan. The Invest Manager is embedded in the existing open banking platform, which can also be used by customers of other principal banks, making it accessible to a broad range of retail customers in the Austrian and, potentially in the future, in the entire CEE region.

An interesting value proposition of the Invest Manager compared to conventional robo-advisors is the security selection, which is based mainly on actively managed funds. The underlying philosophy is to outperform ETFs or ETCs by selecting funds with a proven track record of good performance.8

Erste Asset Management and SimCorp’s collaboration focuses on the rebalancing and order generation algorithm at the core of the Invest Manager (Figure 3). The precise scope consists of aligning each client portfolio to the chosen strategic asset allocation which is submitted via an external web interface. The main challenge was to simultaneously meet three targets:

  1. Avoid contract breaches, short positions and negative cash balances.
  2. Rebalance preferably only positions with high deviations from the asset allocation to avoid unnecessary trading costs and realization of (opportunity) losses too early.
  3. Terminate this algorithm within the limited available timeframe.

These goals are partially contradictory because the avoidance of undesired events (1) and the smart rebalancing logic (2) increase the run time of the algorithm, making a termination within a given timeframe more difficult (3). The implemented approach can be summarized as follows:

  1. A set of rules to prevent undesired events, both of contractual and economical nature, is defined:
    i. Contractual breaches could be a deviation from the strategic asset allocation which is higher than a predefined threshold or the issuance of orders that would result in short positions.
    ii. An example for an economically undesired limit violation is the occurrence of negative cash balances resulting in increased capital requirements for the financial institution.
  2. A logic for the rebalancing algorithm is developed:
    i. Asset classes or positions are rebalanced only if their weight within the portfolio deviates significantly from the client’s asset allocation.
    ii. If this first rebalancing leads to subsequent significant deviations of other asset classes or positions, they are also rebalanced.
    iii. After several potential rebalancing steps, the client’s portfolio weights are in line with his or her strategic asset allocation.
  3. The available timeframe for the order issuance is determined:
    i.The algorithm guarantees that all issued orders comply with the rules of (1) and the logic of (2).
    ii. It also ensures to create all orders soon enough to be in line with the daily investment process.
Digitalization of wealth management

Figure 3: Focus of collaboration between EAM and SimCorp in the Invest Manager

Digitalization of wealth management

Figure 3: Focus of collaboration between EAM and SimCorp in the Invest Manager

The investment algorithm is defined by a portfolio manager, and hence any manual interaction would only be of administrative nature. Such administrative tasks are of no value for the final customer and occupy time in which portfolio managers can instead focus on value-adding activities, such as conducting research, actively managing clients’ money and programming algorithms. While the automation of administrative tasks is spearheaded by the Invest Manager and an absolute must in retail Wealth Management, some of the developed solutions are planned to be applied in traditional Wealth Management at a later stage.

After the successful rollout in Austria in early 2020, the ambition for EAM is to introduce the Invest Manager in other CEE countries. This is in line with the strategy of Erste Group to strengthen its position as the leading financial services provider in the region. For a targeted go-to-market approach, regional particularities will be taken into consideration, with no 1:1 rollout of the Austrian Invest Manager solution to take place. In addition, the improvement of the overall user experience is an important focus area: the handling of the user interface shall become even more transparent and intuitive and the recommendation of portfolios deriving from clients’ preferences shall be further individualized. Finally, the integration of trending investment topics is continuously discussed to maintain and extend the leading position in the hot field of robo-advisors.


1. Grégoire Tondreau, Axel Bohlke, Frederick van Gysegem: ‘Roland Berger Focus – Robo-advisory in Belgium’, May 2019, https://www.rolandberger.com/publications/publication_pdf/FOCUS_Robo-advisory-in-FS.PDF
2. Refer to footnote 1 and Statista.com: ‘Value of assets under management of selected robo-advisors worldwide as of September 2019’, September 2019, https://www.statista.com/outlook/337/100/robo-advisors/worldwide
3. Daniel O’Keefe, Jonathan Warmund, Ben Lewis: ‘Robo Advising’, KPMG, 2016, https://home.kpmg/content/dam/kpmg/pdf/2016/07/Robo-Advising-Catching-Up-And-Getting-Ahead.pdf
4. Dominik Moulliet, Julian Stolzenbach, Alexander Majonek, Thomas Völker: ‘The expansion of Robo-Advisory in Wealth Management’, Deloitte, August 2016, https://www2.deloitte.com/content/dam/Deloitte/de/Documents/financial-services/Deloitte-Robo-safe.pdf
5. The Economist: ‘Silicon speculators’, October 28, 2017, https://www.economist.com/finance-and-economics/2017/10/28/silicon-speculators
6. Facundo Abraham, Sergio Schmukler, José Tessada: ‘Robo-advisors: Investing through machines’, April 1, 2019, http://blogs.worldbank.org/developmenttalk/robo-advisors-investing-through-machines
7. ‘Erste Group at a glance’, https://www.erstegroup.com/en/news-media/erstegroup-at-a-glance
8. ‘Invest Manager – Whitepaper’, https://cdn0.erstegroup.com/content/dam/at/spk-sgruppe/www_sparkasse_at/anlegen/invest-manager-whitepaper.pdf, May 2020.

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