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Robo-Advisory: Opportunities and Risks for the Future of Financial Advisory: Recent Findings and Practical Cases

  • Technical University of Darmstadt

Abstract and Figures

Without professional advisors, taking financial risks is a challenging task for most private households (retail investors). Across countries, digital financial advisory services, in particularly robo-advisors, are becoming more popular in retail and private banking. These tools support their users in financial decision-making, like risk-measurement, portfolio selection, or rebalancing. Recent studies suggest that in the long-term, they could supplement human financial advisory. This work illustrates the key concepts of this (r)evolution, and discusses strengths, weaknesses, opportunities and risks of robo-advisory. The results suggest that robo-advisors have a huge potential to shape the future of the financial advisory industry, despite the fact that there is still a lot of potential yet to be exploited.
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Robo-Advisory – Opportunities and Risks for the Future
of Financial Advisory
Dominik Jung, Florian Glaser, Willi Köpplin
Abstract: Without professional advisors, taking nancial risks is a challenging task for most
private households (retail investors). Across countries, digital nancial advisory services, in
particularly robo-advisors, are becoming more popular in retail and private banking. These
tools support their users in nancial decision-making, like risk-measurement, portfolio
selection, or rebalancing. Recent studies suggest that in the long-term they could supplement
human nancial advisory. This work illustrates the key concepts of this (r)evolution, and
discusses strengths, weaknesses, opportunities and risks of robo-advisory. The results suggest
that robo-advisors have a huge potential to shape the future of the nancial advisory industry,
despite the fact that there is still a lot of potential yet to be exploited.
1 The Rise of Robo-Advisory
In order to assess the status quo of the digital transformation in the German consulting
market, Demand and usage of digital services in nearly all parts of consumers’ lives keeps
increasing - including banking activities such as online debit accounts and online payments,
which are the norm rather than the exception now (Praeg et al. 2015, 2016). As part of
progressive digitalization, growing e-commerce business and the new opportunities
introduced by algorithmic trading, so called robo-advisors entered the market, providing a
fully automated investment experience for a large range of potential customers (Jung et al.
2018, 2017; Sironi 2016). These digital advisors do not only create and invest into a portfolio,
shaped by the individual risk preference of the investor, but also manage the portfolio
afterwards, being aware of market changes and changes of asset characteristics. This seems to
make it a potential substitute for the use of personal advisors, not least because it may shrink
management fees from 0.89 to 0 per cent as analysed in the U.S. (Schapiro 2017). In addition,
it makes advisory services accessible to not only wealthy clients but also investors with lower
investable amounts.
The potential of the robo-advisory concept as seen by venture capitalists and other
investors is huge. For example, in 2015 venture capitalists invested approximately $300
million in the business, believing in the disruptive potential they obtain (Wong 2015).
Furthermore, some forecasts even see robo-advisory adding up to over 10 per cent of the
D. Jung, F. Glaser, W. Köpplin
Karlsruhe Institute of Technology (KIT)
Karlsruhe, Germany
whole wealth management industry in 2020 as Kocianski (2016) states. These statements and
the fact that digitalization still is one of the most recent trends, makes it interesting to
investigate the concept of robo-advisory by examining its key characteristics and discussing
the future potential for advisory and consulting research.
Therefore, we formulate the following research question
RQ1: How can robo-advisors shape the nancial advisory industry, what are harmful and
what are helpful factors?
The remainder of the paper is structured as follows: In chapter 2, we give an overview
considering the term “robo-advisory”, addressing the historical development of nancial
advisory, and the key functionalities of robo-advisors. The subsequent chapter is concerned
with evaluation of the robo-advisory concept, employing a strategic analysis on strengths,
weaknesses, opportunities and threats of the business, followed by a conclusion.
2 State-of-the-Art of Financial Advisory
The nancial advisory industry is facing a major change in their traditional advisory
business. New, innovative participants joined their business in recent years, driven by the
continuing development of information technology, as well as the need to bring transparency
and accessibility into the long-time established traditional wealth management. In the
subsequent sections of this chapter, we outline how nancial advisory has changed over the
past decades and investigate the concept of robo-advisory. In particular, we analyse general
functionalities and characteristics, dierences and peculiarities in comparison to established
advisory setups, as well as recent market developments concerning the nancial advisory
2.1 Financial Advisory
The main purpose of the nancial advisory industry is to help individuals or institutions to
make proper investments, meeting individual investment goals of investors considering long
or short term investment horizons. This denition goes hand in hand with the denition of
investment advice. As stated in the European MiFID, investment advice represents ”the
provision of personal recommendations to a client, either upon its request or at the initiative
of the investment rm, in respect of one or more transactions relating to nancial instruments”
(Cocca 2016 p.47)1.
1 The Markets in Financial Instruments Directive (MiFID) is a regulation set up by the EU seeking to
improve the competitiveness of EU nancial markets, by creating a single market for investment services and
activities, and to ensure a high degree of harmonized protection for investors in nancial instruments (European
Securities and Markets Authority 2007).
Traditionally, only very auent investors enjoyed the experience of individual investment
advice, not least because of the exorbitant fees commonly charged for this kind of advice.
Nevertheless, the market for nancial advisory is changing constantly and recently opened for
low-budget investors (Jung et al. 2017). During the past decades certain events are likely to
have triggered those changes, as illustrated in Figure 1. In the US, which can be considered
the main market for nancial advisory, the very beginning as it is known today took place in
the 1950s. Personal nancial advice and wealth management in general were exclusive and
highly expensive services targeting so called ultra-high net worth individuals (having a net
worth higher than $30 million in 1950) as the only potential investors who could aord
personal advice, usually represented by rich families (Sironi 2016).
Figure 1: The digitalization of nancial advisory services towards digital platform (Jung et al.
2018; Sironi 2016).
A major change regarding accessibility of nancial services took place in the 1970s as
nancial advisories opened to the US middle class by introducing discount brokers. Discount
brokers are cheaper than traditional nancial advisors, as they do not provide real investment
advice, but simply execute buy and sell orders at a reduced commission. This was
revolutionary, as the stock market was now accessible to a much broader range of potential
investors. Discount brokers still play an important role as they “have acquired a large portion
of AUM (assets under management) since their appearance in the 1970s” (Sironi 2016).
The next game changer occurred during the 1990s, as the industry was facing a big change
in terms of connectivity and accessibility caused by the introduction of the World Wide Web.
With the manifestation of the internet, online trading was theoretically accessible to an even
broader group of public investors, ranging from high net worth to auent investors to retail
investors managing their own portfolios. Human dialogue as exercised by traditional
investment advisors in former days, now seemed to be subordinate for the broad mass of retail
investors and only existed for investors who could aord a full-service advisor. Nonetheless,
new investment opportunities were conned mostly to a specialized group of individuals,
limited to those being trading-orientated individuals (Sironi 2016).
In the following years, information technology developed rapidly. The highly developed
connectivity through computers, smartphones, tablets, etc. as well as the declining
information asymmetry caused by much easier and faster access to information, lowered
transaction costs signicantly. Algorithmic trading (so called AlgoTrading) emerged,
representing fully automated investment vehicles and providing new opportunities to potential
investors. Nowadays, not only technological changes but also an unprecedented familiarity
with social media and digital tools, especially seen in millennials, triggered a radical shift in
behaviour of individuals (Sironi 2016). These developments are the antecedents of new
entrants who are about to revolutionize the market by simplifying user experience, lowering
fees and contextually develop new investment strategies for potential investors - the so called
robo-advisors (Nicoletti 2017).
2.2 Robo-Advisory
The term robo-advior may initially trigger thoughts of a humanoid robot giving nancial
advice, which is correct to only a limited degree. There is no physical presence of a robot, but
a virtual. Robo-advisors are digital platforms that comprise interactive and intelligent user
assistance components, using information technology to guide customers through an
automated nancial advisory process (Jung et al. 2018; Sironi 2016; Ludden et al. 2015).
Breaking down recent robo-advisor conceptualizations (Jung et al. 2018; Tertilt & Scholz
2017; Sironi 2016), robo-advisors dier from traditional advisory services in two conceptual
levels (customer assessment and customer portfolio management) as outlined in table 1 and
described below in more detail.
Table 1: The key characteristics of robo-advisors (Jung et al. 2018).
Private households as target segment:
The target segment is independent of actual wealth
No customer screening or pre-selection process
Public online platform, simple registration process
Automated customer proling:
Self-reporting to quantify an individual’s prole
Questionnaires to measure the risk attitude
Preferences, goals, special interests measured by self-assessment questionnaires
Customer PortfolioManagement
Automated investment process:
The whole investment process of robo-advisors is automated and requires no
human activity for proling/portfolio management.
Asset allocation is based on quantitative optimization
Portfolio rebalancing: active (client interaction) / passive (quantitative only)
Portfolio strategy assessment: Dynamic (adjustments by customer) / static (xed
after initial process)
Passive investment products:
No actively managed nancial products to reduce costs
Instruments with transparent cost structure
Common choice: exchange-traded funds (ETF), exchange-traded commodities
The disruptive potential of robo-advisors induced by these key aspects has several
implications. First, a consequence of the fully automated customer proling and investment
process is the considerably low fee structure and minimum investment (Jung et al. 2017). For
example, in Germany the minimum investment amount ranges from 0 EUR to 10,000 EUR
(Doreitner et al. 2017). Consequently, the raise of robo-advisors resulted in a new low-
budget investor class that has not been served by traditional nancial advisors before. Second,
millennials (people born mid-1990s to early 2000s) are a primary target group of robo-
advisors, as they constitute an investor group rather attracted by using technology than
discouraged by it like older investors (Sironi 2016). According to gure 2, the market
potential is large considering the millennial investor group shown. Because the estimated
break-even is much smaller than the potential market, and therefore the opportunity for robo-
advisory rms to make a prot, this is rendering the market attractive to rms newly entering
the market (Gauthier et al. 2015; Wong 2015). To sum up, the main reasons for robo-advisors
being able to be disrupt traditional advisory business are the cheapness, simplicity to access,
and the attractiveness towards new customers such as millennials and tech savvy investors.
Figure 2: $16–$40 billion of assets for robo-advisors to reach break-even compared to
potential size of addressable markets (Wong 2015)2.
2 Addressable markets are ‘Millennials’, households with less than $250,000 of net worth and households
between $250,000 and $500,000 of net worth.
To reach a considerably large range of investors, stand-alone robo-advisors like Betterment
or Scalable Capital started with this untapped market of tech savvy low-budget investors.
Afterwards, they started targeting auent and high net worth investors, having about
$250,000 of net worth (NW) and more, hence starting to steal customers from traditional
advisors (Sironi 2016). Nowadays, traditional wealth management advisors increasingly
integrate robo-advisory solutions into their business models. The most popular examples of
this hybrid advisory structure are Vanguard Personal Advisor Services and Schwab Intelligent
Portfolios. At the beginning of 2017 they were the largest robo-advisors, with $47bn and
$10.2bn of total AUM (Statista 2017). The next sections are illustrating a more detailed
examination of functionalities provided by robo-advisors.
2.3 Financial Advisory and Robo-Advisors
Robo-advisory services provide an alternative approach to traditional nancial advisory.
Robo-advisory has adapted and digitalized traditional phases and fulls basic functionalities
of nancial advice in wealth management (Cocca 2016). But, contrasting traditional services,
they recombine them into three overall steps (Jung et al. 2018, 2017):
Table 2: The activities of robo-advisors across the advisory process (Jung et al. 2018).
Advisory Activities (based on Jung et al. 2017; Cocca 2016; Sironi
Conguration 1. Financial advisory initiation
2. Customer proling, identifying investment needs and objectives
3. Customer Assessment, measure risk and investment prole of the
client based on proling and by automated/cognitive self-assessment
(age, risk tolerance, investment amount)
Matching 1. Dening investment strategy
2. Implementing the dened strategy
3. Long-term model portfolio (visualization of investment proposal)
4. Money management (intern, extern, hybrid)
Maintenance 1. Monitoring and rebalancing of portfolio, reverting to optimal model
2. Performance and Communication (push/pull, event-driven, narrative,
digital,…), customer retention and information
The principle of robo-advisors as outlined above indicates that robo-advisors are exactly
implementing those steps in an entirely digital process. How this process works and what the
key characteristics of a robo-advisor are is illustrated with an example in the following:
After a potential investor registered on the website and wants to invest, the rst step of
interaction is the evaluation of factors like individual risk aversion and investment horizon,
which are needed later on for the construction of the suggested portfolio. This routine requires
the investor to answer a prepared questionnaire with questions varying between companies
and countries (for an overview see Tertilt & Scholz 2017). A summary of dierent categories
and subcategories for questionnaires is given in table 3. Though Tertilt and Scholz (2017)
indicate that some of the questions do not have a measurable impact on the subsequently
generated choice of equity portfolios, they are the basis for later asset allocations and
investment proposals.3 Schwab Intelligent Portfolios is an example for using a questionnaire
to assess an investors’ risk preference, whereas Wealthfront Inc. uses a behavioural approach
asking only a few questions with multiple possible answers (Park et al. 2016 ; Wealthfront
Inc. 2017). The assessment of individual risk aversion and other factors in this step is essential
for the further suggestion of individual portfolio structure.
Table 3: Overview of the three main categories and subcategories for customer assessment
and proling of robo-advisors (adapted from Tertilt & Scholz 2017).
Category Variables of the Customer Prole
3 The pros and cons of using questionnaires are outlined during the SWOT analysis in the next chapter.
General Information Income, investment amount, job description, source of income,
spending, time to retirement, type of account, working status
Risk Capacity
Dependence on withdrawal of investment amount, income
prediction, investment amount / saving rate ration, investment
amount / total capital ratio, investment horizon, liabilities, saving
rate, total capital
Risk Tolerance
Age, association with investing, association with risk, choose
portfolio risk level, comfort investing in stock, credit based
investments, dealing with nancial decisions, degree of nancial
risk taken, education, ever invested in risky asset for thrill,
experience of drop / reaction on drop / max drop before selling,
family and household status, nancial knowledge, gender,
investment experience, investment goal, investor type / self-
assessment risk tolerance, preference return vs. risk
The use of questionnaires without an initial personal consultation is a big change compared
to traditional wealth management services. Additionally, with the use of a questionnaire the
investor is given a feeling of making a logical choice of a portfolio, which is less because of a
third party’s recommendation, but more because of logical choice of their own opinions
(Sironi 2016). The procedure following the completion of the questionnaire and submitting it
passes fully automatically.
In the next step, each robo-advisor uses dierent approaches for selecting appropriate asset
classes (e.g. stocks, bonds, currencies, commodities) according to each individual investor’s
prole. Asset class selection is based on the answers of the questionnaire and may take into
account individual risk preference, goal types, tax-conditions, and more. For instance, a goal
type could be the building of retirement savings and hence imply a longer overall investment
horizon. Another example is a highly risk-averse person who is likely to prefer bonds with
high credit ratings than in individual stock listings, as the default risk of such bonds is very
low. For a less risk averse individual, U.S. stocks may be included in a portfolio to a larger
extent, because of the high capital growth, ination protection and tax eciency attributes
(Lam 2016). The tax eciency attributes of robo-advisors will be outlined in chapter 4.1. In
summary, the challenge for a robo-advisor is to adequately capture these preferences and
goals from a digital interaction with the customer who might not be able to precisely state his
preferences or biased when trying to do so.
The assets are mainly represented by selected exchange-traded funds, passively tracking
market benchmarks. They are predominantly used, as robo-advisors mostly rely on passive
indexing strategies when investing in dierent asset classes. They consider ETFs to be the
best choice as they are cheap, provide good market liquidity and minimize tracking error,
compared to actively managed funds (Lam 2016).4 Moreover, continuous rebalancing can be
done automatically and investors are linked to market trends instead of trying (but often
failing) to gain more return through active asset management as Arnott et al. (2000) show.
Active management would attempt to achieve above market returns by trading or shorting the
constituents of an index, based on rules, sentiment, or portfolio managers' views (Sironi
4 A tracking error describes the dierence in return between the index fund and the index itself.
2016). Passive mutual funds are also less usual, because ETFs can be traded at any time on
open markets, facilitating automated portfolio rebalancing and tax-loss harvesting (Sironi
2016).5 Furthermore, each robo-advisor has multiple ETFs for each asset class, to shift
between them when, for example, the estimated volatility of one asset is considerably larger
than the estimated volatility of a substitute asset. A variation of the passive indexing strategy
of most robo-advisors is “direct indexing”, in which the underlying stocks of ETFs are
indexed, to achieve even more advanced tax-harvesting purposes, which is outlined in more
detail in the following chapter.6
After successfully identifying which asset classes should be considered, proper portfolio
weights for the dierent asset classes must be chosen, as they indicate how much of a specic
asset relative to the others should be included in the target portfolio. The most prominent and
modern approach on estimating optimal and therefore ecient portfolio weights is manifested
in the modern portfolio theory (or mean-variance analysis) (Markowitz 1952). Basically,
modern portfolio theory constitutes a mathematical framework considering a set of risky
assets and calculating portfolios for which the expected return is maximized for a given level
of portfolio risk (Lam 2016).7 Though there exist many dierent approaches compared to the
basic model, most robo-advisors are using modern portfolio theory or an alteration of it to
determine portfolio weights for the predetermined asset classes (Lam 2016). In a rst step
expected returns and volatilities of the dierent asset classes must be estimated, which is
usually done by analysing the assets’ historical returns and volatilities. After the
determination of the dierent capital market assumptions is completed, mean-variance
optimization is done. The optimization is based on the risk prole previously identied from
the questionnaire of the investor considering the correlations in between the dierent asset
classes. In the end, this approach leads to the successful estimation of portfolio weights (Lam
After the investment is conducted, “algorithmic rebalancing” provides stability of the
portfolio weights and therefore the risk-level, by shifting investments among asset classes to
revert towards its predened risk long-term equilibrium, when the weights of the portfolio
change. This can be triggered by high and low performance of individual assets or by external
factors like for example down going markets. Rebalancing rules are dierent among various
robo-advisors. There can be rules of discrete schedules (e.g. once a month), which has the
disadvantage of reacting poorly to fast changing environmental factors. Referring to Lam
(2016), a more prevalent rebalancing rule is to use thresholds rather than time-based
rebalancing. In this rebalancing model, the weights of asset classes are adjusted when
reaching certain thresholds. For example, if stock ETFs returns are reaching a certain upper
limit, they cause a dierence in portfolio weights compared to the long-term equilibrium.
Subsequently, some of the stock ETFs are sold and put into other asset classes to get back to
5 To name some of the most prominent assets used, one of the stock ETFs Betterment uses is the ‘Vanguard
U.S. Total Stock Market Index ETF’ and one of the bond ETFs is BlackRock’s ‘iShares Short-Term Treasury
Bond Index ETF’ (See Betterment portfolio structure /
1668747052.1496058254, last accessed on 10.07.2017)
6 See, last accessed on 10.07.2017
7 For a compact summary on the function principles and limitations of mean-variance analysis see Lam
the previous weight combination of asset classes. With this approach, each investor stays
invested in their specic risk level, not needing to pay an active investment manager for doing
this job. Furthermore, if risk preferences of an investor change over time, the rebalancing
techniques also adjust the portfolio towards the new target allocation. This can be the case if,
for example, an investor suddenly has a lot more money, resulting in lower risk-aversion
(Lam 2016).
Finally, continuous monitoring and 24/7 accessibility is a given feature of robo-advisors,
dierentiating them from traditional wealth manager services according to Sironi (2016). To
summarize, the key benets of the investment process of robo-advisors are the personalized
approach of using questionnaires, the suggestion of a portfolio based on academic evidence,
the simple use, automated rebalancing as well as continuous monitoring of investments.
3 SWOT Analysis of Robo-Advisory
In the following, we rely on a “strengths-weaknesses-opportunities-threats” or SWOT
analysis (Helms & Nixon 2010) as a methodological tool to investigate the complex market
situation and strategic elements of the current robo-advisory market. Hence, we outline the
strengths and weaknesses of robo-advisors, as well as the external factors it is facing,
represented by opportunities and threats to reduce the quantitative complexity of driving
forces. The purpose of this SWOT analysis is to critically analyse the advantageousness of
robo-advisory and to show what potential robo-advisory is unlocking to transform nancial
advisory as we know it today. Table 4 provides an overview of the key points discussed in the
Table 4: Overview of the key characteristics of the SWOT analysis of robo-advisory.
Helpful Positive Characteristics Harmful Negative Characteristics
Lower fees and minimum investment
Tax-loss harvesting
Investment experience
Portfolio construction by algorithms and
automated rebalancing
Less emotional decision making
Investment costs are not minimized
Conict of interests
Poor assessment of risk tolerance and
lack of personalization
No personal contact
Unfullled duciary duty
Ubiquity of Digital Services
Opportunity to standardize and integrate
Goal-based investing
Complement traditional advisors
Competitive environment
No acceptance of users
Possible threat from regulators
Bearish market and crisis
3.1 Strengths
Lower fees and minimum investment: Two of the key strengths of robo-advisors are the
comparably low fee structure and low minimum investment requirements (Jung et al. 2017).
Taking the U.S. market as an example, minimum investments for robo-advisory services start
at $0 as Meola (2017) shows. Furthermore, fees are also substantially lower because robo-
advisors predominantly invest into ETFs who often have the advantage of an inherent low-
cost structure. In some cases, U.S. robo-advisors advertise “zero fees” as reported by Gauthier
et al. (2015). Nonetheless, the upper limit for direct fees for robo-advisory in the U.S. market
is 0.89 per cent by the U.S. advisor Vanguard Personal Capital Services for investment
amounts below $1billion (Schapiro 2017). Meola (2017) shows that the fee structure of many
robo-advisors is regressive and inversely correlated to the invested amount (that is larger
investments lead to lower fees in percentages and vice versa).
The low fee structure and the low minimum investment of robo-advisors are real game
changers in nancial advisory, as traditional advisors charge fees ranging from 1 to 3 per cent
of the portfolio value for their services (Mercadante 2017). A new eld of potential low-
budget investors can be accessed, previously not noticed by traditional advisory rms.
Additionally, high net-worth investors are potential targets, as they could see the opportunity
to increase prots by reducing fees, assuming that the provided service is of equal quality.
Tax-loss harvesting: Another feature is that robo-advisors are tax-loss harvesting in some
countries (e.g. in the U.S.). The rationale is to sell securities at a loss, take the proceeds and
buy a highly positively correlated substitute asset.8 The capital losses can then be realized and
oset against other capital gains to create a tax saving amount. With this approach, investors
can prot in two ways. First, the tax saving amount can be reinvested to generate a
compounding of the tax saving. Second, due to the dierent tax rates for long- and short-term
capital gains, tax rate arbitrage can be conducted.9 Overall, the risk-return-prole is
maintained because of the high correlation between both the sold and the bought security, but
additional gains are generated. This method is only suitable for long term-investors as both
compounding of tax savings and tax-arbitrage due to dierent tax rates are solely realizable in
the long-run (Lam 2016).
Most robo-advisors are using this tax gap on ETF basis to buy and sell highly correlated
ETFs. Nevertheless, advisors such as Wealthfront advance to even more sophisticated
methods by applying the concept of “direct indexing”, where not the ETFs but their
underlying individual stocks are bought and sold. This makes tax-loss harvesting even more
eective and a useful approach on generating additional gains. Wealthfront suggests that with
“direct indexing” it is possible to add as much as two per cent to annual investment
performance compared to vendors not using tax-loss harvesting.10
8 It is not possible to buy the exact same security as this would be a ‘wash sale’ and not permitted in the U.S.
(see, last accessed on 10.07.2017)
9 Long-term (> 1 year holding time of the investment) tax rate on capital gains is higher than the short-term
tax rate in the U.S. Therefore tax-arbitrage can be done by not realizing capital gains before a holding time of
one year (Lam 2016).
10 See, last accessed on 10.07.2017
Tax-loss harvesting is value-adding for long-term investors compared to individual
investments as tax matters can be outsourced, without the need to consult an expensive
personal advisor. Nonetheless, the usefulness of tax-loss harvesting is limited to countries
using dierent tax rates for long- and short-term capital gains. In Germany, for example, a
uniform tax rate prevents tax arbitrage of this type.
Investment experience: The entirely digital approach of robo-advisory is an advantage
regarding the ease of use. Especially in times where smartphones, tablets and other wearables
are ubiquitous, this concept seems to be promising. In particular, millennials and people with
a high anity to technology are targeted here. Furthermore, due to the transparency and
graphical presentation provided on the advisors’ websites, the consumers feel that they can
understand the oer without ambiguity, are granted easy access to the platforms and feel they
can aord it (Sironi 2016). This single-mindedness of robo-advisory is also manifested in the
questionnaires, as with answering them investors perceive the model portfolio more as a
logical choice of their own opinions rather than as an external advice given by a nancial
advisor (Sironi 2016).
Established algorithms and automated rebalancing: Portfolio optimization of most
robo-advisors is based on modern portfolio theory using eminent approaches from Markowitz
(1952) and alterations thereof. The advanced risk management resulting from its use becomes
apparent by taking a look at the performance of robo-advisors. A study conducted by Reher
and Sun (2016) shows that they outperform both self-managed as well as mutual fund
portfolios (on a risk adjusted basis). This speaks for the modern portfolio theory as well as for
the passive investment strategy of robo-advisors. Nonetheless, they also investigated the
“mutual fund stickiness” of investors to mutual funds, that is, investors do not seem to switch
to robo-advisory even when robo-advisors apparently generate higher prots. Furthermore,
though modern portfolio theory is highly contemporary, there are still diculties in the
assessment of short-term strategies, which indicates that robo-advisors are more valuable in
the long-run as in the short-run (Nicoletti 2017). The concept of automated rebalancing,
which formerly had to be done by active managers, also speaks for robo-advisors. As Lam
(2016) indicates, active managers may not keep pace with changing weights of asset classes in
a customer portfolio, resulting for instance from higher returns in one of the asset classes.
This can lead to return slippage and delayed risk adjustment, which is avoided by robo-
Less emotional decision making: The last major strength of robo-advisory services,
however, is concerned with the emotionality of investors. Average investors may build a
portfolio and when the market turns down for some reason they immediately panic and sell it.
With this habit of buying-high and selling-low they may miss potential rises in prices after
they sell the securities (Tra 2016). A study of Vanguard shows that nancial advisory can
outperform regular retail investors by up to 3 per cent, which is partly because professional
investor are less emotionally driven (Kinniry et al. 2016). In accordance with these ndings
robo-advisors are likely to be advantageous as they are not driven by emotions. If markets go
up or down, algorithmic trading algorithms should prevent them from making ‘wrong’
choices and furthermore should help to stick to a consistent risk level.
3.2 Weaknesses
Investment costs are not minimized: The aspect of low fees compared to traditional
advisory rms as outlined in the last chapter, is also facing criticism. Fein (2015) criticizes
robo-advisors for advertising their products to be „low fee“ or „zero fee“ services which does
not necessarily have to be true. She states that “robo-advisor users typically bear the cost of
brokerage, transaction, and other fees and expenses, whether directly or indirectly, and thus
contribute to the robo-advisor’s compensation” (Fein 2015). Though fees of robo-advisors are
comparably low because of the low-cost structure of ETFs and no need of advisory personnel,
Fein’s investigation seems to be argumentative. As robo-advisors still have to defray costs
and moreover have intentions to run a protable business, with real ‘zero fees’ charged to the
customer this is not possible. Indirect fees are often not explicitly shown in the investment
dashboards, and hence masked as lower returns of investor portfolios.
Furthermore, Fein (2015) states that robo-advisors reserve the right to change the fees at
their discretion. This can be an indication for advisors using the low-fees as an initial feature
to attract new customers until they have a large enough customer base and increasing the fees
afterwards to generate higher prots.
Conict of interests: A report from FINRA (2016) is calling attention for customers of
robo-advisory services to be aware that robo-advisors do not necessarily eliminate conicts of
interest present in personal nancial advisory – quite the contrary might be true.11
Robo-advisors maintain relationships to aliated brokers, clearing rms, custodians and
other rms to provide the service of automated investment to their customers. Fein (2015)
investigated these relationships to identify whether conicts of interests between customers
and robo-advisors are a present issue. For instance, she identied that some companies do use
brokers of their choice, demanding a higher price than favourable for the customer (Fein
2015). This indicates that a share of the higher price is kept as prot by the robo-advisor.
Especially for “zero fees” robo-advisors, this conict of interest seems to be present. If they
would not exploit the possibilities of indirect fees they would not generate any revenue. In
particularly, some robo-advisors are disclosing that they “may have an interest or position in
securities that are recommended to clients” which indicates an additional conict of interest
like commission payments for these instruments (Fein 2015). Consequently, there exist
conicts of interest on two conceptual levels: rstly, concerning the aliated brokers, and
secondly concerning the selection of recommended assets.
Since conicts of interest potentially exist in the received investment advice, customers of
robo-advisory services should assess whether those conicts compromise the objectivity of
investment advice given by the robo-advisor. However, this assessment might be as
challenging as managing the portfolio yourself. It is worth noting, that these conicts of
interest are quite similar to those that are present in traditional nancial advisory.
Poor assessment of risk tolerance and lack of personalization: A commonly expressed
point of criticism refers to the risk assessment of robo-advisors based on prepared
questionnaires. A report of the SEC and FINRA (2015) alerts investors that the questionnaires
11 The Financial Industry Regulatory Authority is regulatory institution in the U.S. mainly concerned with
participants in securities trading (see www., last accessed 16.07.2017).
they ll to identify what individual investment strategy suits best, is often not taking into
account the individual goals and may be based on incorrect additional assumptions,
incomplete information or circumstances not relevant for the investors (for example age, time
horizon and individual goals).12 Cocca (2016) shares this view, assessing the simplicity of risk
tolerance questionnaires as ‘natural’ limit in the development of robo-advice.
Furthermore, Tertilt and Scholz (2017) recently added research regarding the use of
questionnaires to assess risk levels. They found that of the robo-advisors they investigated,
which are based in Germany, the U.S. and the U.K., only 60 per cent of the asked questions
have an impact on the risk categorization. In addition to that, they found robo-advisors to
give similar equity recommendations as human advisors, challenging the claim of robo-
advisors to be per se more valuable and unbiased than traditional nancial advisors.
Having a look at the rebalancing mechanisms of robo-advisors, lack of personalization and
(possibly) wrong advice are also present. The rebalancing rules outlined in chapter 2.3 are
fairly simplistic and therefore limitedly suitable (Sironi 2016). For example, Schwab
Intelligent Portfolios ignores individual requirements and inserts the investor into a one-size-
ts-all rebalancing algorithm (Marotta 2015).
To summarize, it seems that due to the lack of personalization in risk assessment and
rebalancing, robo-advisory does not meet the need of investors with moderately complex
requirements so far.
No personal contact: One of the main features of robo-advisors is that there is no need for
a personal advisor anymore. On the one hand, this can be an advantage as fees are getting
lower and advisory services are brought to a broader mass of customers. On the other hand,
the question arises whether impersonal investment advice is something a customer actually
wants (Jung et al. 2017). Regarding a survey conducted in Italy in 2016, 49 per cent of the
polled persons would not use robo-advisory services without the support of an in-person
consultant and only 11 per cent are willing to rely on an autonomous robo-advisor (Nicoletti
Especially in wealth management, where traditionally high net worth clients are present,
personal contact seems to be more important, as for example “a customer may feel more
willing to talk about the consequences of the sudden passing away of their wife when the
long-time advisor of the family addresses this delicate issue with due care, whereas a robo-
advisor might send a change of text of the testament contract by e-mail on the basis of the
calculated probability of such a scenario” (Cocca 2016). This example illustrates the diculty
of not having a personal relationship with the nancial advisor. Moreover, clients are left to
their own devices when they try assessing whether the provided investment strategies meet
their needs and goals (Fein 2015).
Unfullled duciary duty: In the U.S., under the Fiduciary Standard of Care, broker-
dealers are obliged to act in their clients’ best interest when giving nancial advice.13 Though
not giving personal advice, a robo-advisor also acts as an advising duciary under the
Investment Advisers Act of 1940 and is obliged to meet the duciary standards (SEC 1940).
12 The SEC (U.S. Security Exchange Commission) is an independent exchange supervisory authority in the
U.S. overseeing securities trading in the U.S. (see
13 Broker-dealers are individuals or rms doing both executing investments on their clients’ behalf (broker)
and trading for its own account (dealer).
Due to the lack of personal investment advice, in a more recent study Fein (2016) investigates
the duciary implications following this shortage.
She indicates that robo-advisors do not provide overall portfolio analysis because they
assess investments in isolation (for each asset class) and do not develop an overall investment
strategy on behalf of the client. Moreover, Fein (2016) states that robo-advisors only limitedly
consider individual client conditions or other external factors, which can lead to fatal
consequences, for example in plummeting market scenarios. Finally, she concludes that only
professionally trained advisors can reliably manage portfolios (Fein 2016).
3.3 Opportunities
Ubiquity of Digital Services: The nancial industry and wealth management is in constant
demographic and technological change. Consequently, behaviour of consumers is also
shifting. People are pervasively connected via smartphones, tablets, notebooks and other
devices, everywhere at any time. Especially younger generations grow up with technology
and are therefore much more used to it (Sironi 2016). Today’s digitalization of every day’s
life provides good foundations for robo-advisors to establish in a market that is dominated by
conventional wealth and asset management institutions. The strategy of choice in this
environment seems to be reaping the cost reductions enabled by technology while still acting
as an expensive intermediary (Cocca 2016). Robo-advisors are quitting this concept of
Indeed, Gauthier et al. (2015) states that robo-advice is explicitly targeting the younger,
tech savvy generation. Cocca (2016) shows in his study that 30 per cent of respondents of a
survey conducted in Europe can imagine to use a purely automated investment advisor,
whereas this percentage rises to 45 per cent when excluding the population at age 60 or above
as they tend to stick to their accustomed habits. The potential market represented by
millennials is currently below $2 trillion in the U.S., which is signicantly lower compared to
the total AUM of the asset management industry. However, the market keeps growing year by
year alongside the degree of digitalization of societies (Wong 2015).
Opportunity to standardize and integrate: Apart from services like portfolio rebalancing
and automated asset allocation robo-advisors currently provide, the wealth managements’
industry oer is still considerably larger. They provide services of higher complexity which
are hard to standardize and mass-customize by robo-advisors, including for example cross-
border tax-advice, which is not only considering dierent national tax laws but also
international tax law and individual tax arrangements among countries. The full integration
and combination of nancial, legal and tax issues on an international scale is a very complex
process, where a large number of degrees of freedom appear (Cocca 2016).
Nonetheless, the opportunity to standardize and integrate more complex services is a key
factor in attracting more customers currently relying on a traditional personal advisor. As
Cocca (2016 p.51) states, “the question of who can bring together a comprehensive total
oering is currently in the background, but it is likely to be very relevant in the future”.
Established traditional advisors launching individual advisors or acquiring stand-alone robo-
advisors may have an advantage here, as they have decades of experience considering wealth
and asset management practices and knowledge.
Goal-based investing: The simple approach of assessing customers risk proles using
questionnaires and applying concepts of the modern portfolio theory for portfolio
optimization has been discussed previously. To prospectively solve these problems of
simplication, assuming the market volatility to be the setscrew of return, the philosophy of
goal-based investing is the long-term goal of the robo-advisory business, as it diers
fundamentally from mean-variance optimization concepts. Following goal-based investing,
the individuals themselves, composed of multiple individual values, investment goals,
priorities, time horizons and risk proles, are the centre of attention (Sironi 2016). As every
human diers in the combination of the previously stated attributes, a much more individual
model must be developed to reach a high degree of personalization and optimally meet
customer needs.
Gauthier et al. (2015) as well as Ludden et al. (2015) see high future potential of advanced
personalization of robo-advisory services, not least because of contemporary technological
developments like big data analytics to assess goals and fears of investors. Rudimentary
aspects of advanced personalization are already implemented in some robo-advisors. Motif
Investing, for example, gives the investor the opportunity to choose between three “impact
portfolios”, namely “sustainable planet”, “fair labour” and “good corporate behaviour”. Each
of these investment tracks chooses only assets meeting one of the three previously chosen
individual goals mixed up with mean-variance optimization.14 This is only a rst step towards
goal-based investing, as there is a severe limitation on numbers of goals and no such
individual goals as for instance college savings, planned home purchases, retirement,
protection needs, etc. included, but in the long-run, robo-advisors will head the direction of
goal-based investing as Sironi (2016) states.
Complement traditional advisors: The low amount of AUM currently held by stand-
alone robo-advisors is a problem for them, as it seems that they do not attract enough high-net
worth customers. Although they have high growth rates, the current total market share of the
wealth management market attributable to robo-advisors is still below 1 per cent in 2016
(Cocca 2016). This is not necessarily crucial, as Wong (2015) states the break-even of robo-
advisors to be considerably low, but they act in a competitive area and have lots of marketing
costs to increase their reach and to acquire new customers (Sironi 2016). According to
Burnmark (2017), it costs a US robo-advisor $389, on average, to acquire a customer, while
the average account size of $27,000 only produces a revenue of $90.
A possible solution for this problem could be the combination of aspects or even whole
businesses of traditional and robo-advisory. Gauthier et al. (2015) analysed potential scenarios
of interactions between stand-alone robo-advisors and existing traditional advisors. One part
of their study considers the partnering of existing robo-advisors and banks which is benecial
for both, as wealth managers can oer robo-advised portfolios and robo-advisors prot from
existing knowledge and client base of wealth managers. Sironi (2016) also recognized this
development, appropriately naming it a transformation “from B2C robo-advisors to B2B2C
14 See https://www.moti, last accessed 03.07.2017
robo-4-advisors”. Betterment and its platform “Betterment 4 Advisors” is a recent example of
this, providing the integration of robo-services for traditional advisors.15
Sironi (2016) also notices the transformation of existing robo-advisors to B2B business
models, providing cloud services for nancial institutions that want the technology of robo-
advisory but do not have the capabilities to build their own solution. Furthermore, robo-
advisors could extend their portfolio and include also personal advisors, to attract customers
not willing to rely only on implemented algorithms. For example, Betterment announced to
add live advisors to its oering, available to investors with AUM of $100,000 or more,
making it a hybrid service (Anderson 2017).
3.4 Threats
Competitive environment: A future threat for every robo-advisor in the market is the high
competition they are facing. According to Burnmark (2017), in Europe currently more than 70
robo-advisors exist, each of them following similar investment principles. Moreover, in the
U.S. there exist even more than 200 robo-advisors, each competing for customers. This
competitiveness is currently mostly restricted to domestic markets, but if regulatory
environments, investment practices and other barriers to entering foreign markets change,
international competition will rise, and smaller companies will face U.S. companies, which
are considerably larger in terms of AUM (CB Insights 2017).
Furthermore, stand-alone robo-advisors are recently facing robo-advisors founded by
traditional advisory rms. Introduced in 2015, Schwab Intelligent Portfolios and Vanguard
Personal Advisor Services, which are such robo-advisors, gradually overtake the largest US
FinTechs, Betterment and Wealthfront, in terms of AUM (Malito & Zhu 2016). Primarily, this
is a result of the large customer base which traditional advisors already have when launching
their own robo-advisor. This can be a major threat for stand-alone robo-advisors as they need
time to acquire a competitive customer base and become protable. Wong (2015) investigated
when robo-advisors start getting protable and states that the break-even client asset level for
robo-advisors ranges from $16 billion to $40 billion, which is 8–20 times the current client
asset level of leading robo-advisory rms, which suggests that most advisors are likely to be
underfunded at this stage. Therefore, most of the current stand-alone robo-advisors will face
strong competition, and hence will hardly survive the next decade without adjusting to the
changing market conditions by enhancing their customer base and increasing their
No acceptance of users: As emphasized before, the shift in behaviour and increasing
anity to technology is indicating millennials as an attractive target for robo-advisors
(Gauthier et al. 2015). Nevertheless, this does not necessarily mean that a broad mass of
potential users will actually consider robo-advisors as a real opportunity compared to retail
investing or a personal advisor.
Following a survey conducted by Ludden et al. (2015), they indicate that 77 per cent of the
surveyed wealth management clients trust their nancial advisors. Moreover, 81 per cent say
that face-to-face interaction is important. This indicates that personal advisory service is still a
15 See, last accessed 03.07.2017
highly desired feature of nancial advisors. Furthermore, they take up the position that current
robo-advisors do not meet the needs of investors with complex nancial lives, as the
implemented approaches for, e.g., risk assessment are too plain (Ludden et al. 2015).
A study conducted by Reher and Sun (2016) shows that mutual fund holders stick to their
mutual fund investment, even when a comparable robo-advisor signicantly outperforms the
mutual fund. This is an indication for the missing trust in robo-advisors, though it may also be
attributed to other reasons such as higher costs or laziness to switch investments. As a result,
robo-advisors are starting to launch hybrid solutions, providing a personal advisor besides the
robo-advisory service. For instance, Betterment recently introduced a call centre of certied
nancial planners to monitor accounts and provide advice.16
The argument of trust is also discussed by Cocca (2016), stating that, eventually, it will
depend on the individual preferences of customers to receive advice based on an algorithm or
provided in person. Nonetheless, the question of broad acceptance of the robo-advisory
concept in the future remains uncertain, constituting a potential threat for the business.
Possible threat from regulators: Recently, many institutions and individuals uttered
rising concerns regarding regulation of digital investment advisory for several reasons. For
instance, Fein (2015) is concerned about potential conict of interests, the lack of personal
advice and utters duciary concerns as outlined in chapter 4.2. Furthermore, Novick et al.
(2016) provide a good overview about which institutions recently addressed concerns
regarding regulation on robo-advisors. FINRA (2016) and SEC and FINRA (2015) raise
concerns regarding supervision of algorithms, conicts of interest, practices on customer
proling and automatic rebalancing, showing that there is a need on advanced regulation of
robo-advisors. Moreover, under the duciary rule released by the Department of Labor
(2016), U.S. digital advisors “need to evaluate whether they need to make changes in their
programs to ensure that they are compliant with ERISA duciary requirements”.
Baker and Dellaert (2017 p.4) also see a need of regulators represented by lawyers,
economists, and behavioural scientists already involved in nancial services regulation, to
extend their range of competencies “to understand enough about computer and data science to
craft and apply new regulatory strategies”. They also mention the need to regulate robo-
advisors because of the new scale of risks, as all of their investors follow similar algorithms
(Baker & Dellaert 2017).
These recent developments and rising concerns about regulation on digital investment
advice constitute a potential threat for the robo-advisory industry. If regulation gets stricter in
the future, this could have impacts on protability due to increased compliance costs.
Bearish market and crisis: A point many investors are concerned about is that currently
established robo-advisors never experienced a crisis or bearish market. Among others, Fein
(2015) states, that she is concerned about how a robo-advisor will perform in a market
downturn. Tra (2016) argues that robo-advisors have the advantage of making practically no
emotionally-driven decisions, and would perform better than a retail investor or even a
traditional nancial advisor, as both carry individual emotional and cognitive biases. This can
be illustrated with a recent example. In 2016, during the referendum on the Brexit in the U.K.,
many investors panicked and sold their positions impulsively, missing the later opportunity of
16 See, last
accessed on 10.07.2017
rising stock prices. This is avoided by robo-advisors, as they stick to the predened risk
determination, reallocate between certain assets and asset classes and do not panic in similar
situations. This can be an opportunity for robo-advisors.
Nonetheless, the question how robo-advisors will perform in extreme market downturns
like during the nancial crisis in 2008 remains unanswered. Consequently, it is unclear
whether trust in robo-advisors is big enough to not sell their positions proactively, even in
times of extreme market shifts or if a personal advisor is considered more trustworthy in such
4 Conclusion
In the preceding discussion, we gave an overview about the changing landscape of
nancial advisory as well as the functionality and market developments of robo-advisory.
Furthermore, we critically analysed the robo-advisory concept, outlining current strengths and
weaknesses as well as opportunities and threats. We outlined robo-advisors to be an
innovative, new approach that is transforming nancial advisory as it is known today.
However, there is still a lot of potential yet to be unlocked.
Being investment approaches with low costs, high transparency and good performance
compared to mutual funds, as Reher and Sun(2016) show, they address the low-wealth
investor group which has not been targeted by incumbent wealth and asset management rms.
Furthermore, most robo-advisors rely on passive index investment strategies in combination
with amendments of the modern portfolio theory introduced by Markowitz (1952), thus
relying on a scientic approach. Nevertheless, robo-advisors face a lot of scepticism,
especially from regulatory authorities. They have raised concerns regarding conict of
interests, the poor assessment of risk tolerance, the missing personal contact and
consequentially the unfullled duciary duty towards investors and regulatory authorities.
On the other hand, regarding future developments, there are opportunities to be leveraged
by robo-advisory. The shift in behaviour due to the intensied use of electronic devices might
render robo-advisory more attractive for investors in younger generations. Furthermore, goal-
based investing and the individual modelling of goals, values and time horizons might be the
next steps taken by robo-advisory. Opportunities of business transformation of stand-alone
robo-advisors emerge through cooperation with existing nancial institutions in order to
switch from B2C models to B2B2C, as Sironi (2016) pointed out.
Despite these promising upsides, there are threats the industry is facing. First, the
environment is getting increasingly competitive. Considering Europe, there exist more than 70
individual robo-advisors, each of them competing in the same potential market (Schapiro
2017). Second, it is unclear whether users will accept fully automated investment devices,
without any personal contact. Third, regulation can be a future threat as authorities like
FINRA or the SEC recently uttered their concerns (SEC & FINRA 2015, FINRA 2016).
Consequently, the rise of robo-advisory has just begun, and it is questionable to what
extent it defends its existence facing bearish markets for the rst time. Many questions
regarding design, implementation and regulation are still unanswered and therefore robo-
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... Entire end-to-end value chains are envisioned to be changed or replaced [31]. Digital business models promise to be more cost-effective [40], to be more accessible, and to attract potential new customer segments [33,38]. This all is assumed to help consulting firms to stay competitive [36]. ...
... We found that there are few arguments against the digitalisation of IT consulting. Only five papers [19,32,33,40,42] out of our 20 address this aspect. Emrich et al. see 'higher complexity, higher dynamics [and] higher uncertainty' ( [32], p. 1309) regarding digitalisation activities. ...
... Emrich et al. see 'higher complexity, higher dynamics [and] higher uncertainty' ( [32], p. 1309) regarding digitalisation activities. Other authors state that there has been only little experience with digital services until now, which rises concerns [33]. Examples of such concerns are related to the acceptance of digital services on both the clients' and consultants's sides, the need to establish a change process to be successful [40], the limited customisability, and the reduced personal contact or its total loss during the service provisioning [33]. ...
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Currently, the digital transformation of the provisioning of IT consulting services to clients forces many companies to reinvent their business models. While the importance of digital transformation is well recognised, very little has been done until now to closely examine the concepts characterising this phenomenon in the domain of IT consulting. This paper aims at mapping out what is currently known in scientific literature about the digitalisation of IT consulting service provisioning and its characterising concepts and approaches. Analysing 20 selected articles, we identified 10 concepts characterising the digital IT consulting and 14 areas considered to be candidates for inclusion in the design of digital IT consulting services. Moreover, we found only one approach to the digitalisation of service provisioning that was empirically evaluated. We conclude that (i) the digital transformation is multifaceted and characterised through many aspects; (ii) digital IT consulting services are primarily explored from business perspective, while research of stakeholders' needs, technical requirements, and designs of relevant artefacts is scarce; (iii) while individual solutions and practices that worked in real-world contexts are reported, very little empirical evaluation was done. Finally, we reflected on the limitations and on the theoretical and practical implications of this work.
... Robo-Advisors (RAs) guide investors through an automated financial advice process, recommend personalized portfolio assignments based on their risk-affinity and financial goals, and monitor as well as rebalance their portfolios automatically over time . RAs aim at giving more people access to diversified capital market participation, including retail investors with small amounts of capital to invest and often low financial knowledge (Jung et al., 2019;. Thereby, RAs take on the role of traditional financial advisors and have promising attributes, e.g., digital accessibility independent of time and location, a certain degree of performance at low costs, lower minimum investment amounts as well as consistent and automated decision making, which reduces risks and decreases psychological pitfalls (e.g., Beketov et al., 2018;Jung et al., 2019). ...
... RAs aim at giving more people access to diversified capital market participation, including retail investors with small amounts of capital to invest and often low financial knowledge (Jung et al., 2019;. Thereby, RAs take on the role of traditional financial advisors and have promising attributes, e.g., digital accessibility independent of time and location, a certain degree of performance at low costs, lower minimum investment amounts as well as consistent and automated decision making, which reduces risks and decreases psychological pitfalls (e.g., Beketov et al., 2018;Jung et al., 2019). On the other hand Robo-Advice (RA) has potential weaknesses, e.g., a poor assessment of users risk tolerance and a lack of sufficient personalization which can lead to an unfulfilled fiduciary duty and less user acceptance (Beketov et al., 2018;Jung et al., 2019). ...
... Thereby, RAs take on the role of traditional financial advisors and have promising attributes, e.g., digital accessibility independent of time and location, a certain degree of performance at low costs, lower minimum investment amounts as well as consistent and automated decision making, which reduces risks and decreases psychological pitfalls (e.g., Beketov et al., 2018;Jung et al., 2019). On the other hand Robo-Advice (RA) has potential weaknesses, e.g., a poor assessment of users risk tolerance and a lack of sufficient personalization which can lead to an unfulfilled fiduciary duty and less user acceptance (Beketov et al., 2018;Jung et al., 2019). ...
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This cumulative dissertation consists of and critically discusses five peer-reviewed and co-authored publications by me about the digitalization and automation of financial advice, so-called Robo-Advice (RA). The dissertation thereby covers two main research areas. The first research area addresses the positioning of RA in science and practice providing a structure for more in-depth analyses. From a scientific perspective the relevant literature about RA is systematized in a new Organizing Framework for RA Research. The practical positioning of RA in the broader landscape of FinTech is approached in the context of mobile personal finance applications. The second research area addresses the focused analysis and design of certain RA components. One study focuses on the business model of RA, aiming at understanding their distinct elements and finding major similarities and differences. A second study focuses on RA portfolio recommendations, providing an understanding of how they differ, especially in structure, selected products, performance, and risk. Lastly, a third study presents meta-requirements and design principles for RA addressing the problem of unethical behavior that can decrease trust and the adoption of RA.
... XX wieku, kiedy to amerykańskim inwestorom detalicznym zaoferowano usługi "tanich brokerów" (discount brokers), tańszych niż tradycyjni doradcy, ponieważ nieświadczących usług doradczych, a jedynie wykonujących zlecenia kupna/sprzedaży papierów wartościowych za niską opłatę wpierw za pomocą systemów offline 2 , a potem online 3 . W połowie lat 80. XX wieku zaoferowano zarówno inwestorom bogatym, jak i detalicznym dostęp do internetowych platform obrotu papierami wartościowymi z wbudowaną opcją pełnego i niekosztownego doradztwa inwestycyjnego 4 , Niecałe dwie dekady później nastała zaś era handlu algorytmicznego, która -wraz z upowszechnieniem się szybkiego dostępu do internetu w komputerach, smartfonach czy tabletach -spowodowała zmniejszanie się asymetrii informacji i obniżanie kosztów transakcyjnych (Jung, Glaser i Köpplin, 2019). Dynamiczny wzrost popularności korzystania z narzędzi cyfrowych wywołał ważną zmianę w zachowaniu osób indywidualnych, które obecnie coraz częściej komunikują się ze światem przez media społecznościowe, a nie osobiście, i nierzadko tym właśnie kanałem poszukują usług finansowych, pomijając tradycyjnych pośredników finansowych (Sironi, 2016). ...
... Doradcy ci skupiają się dziś już nie na analizie portfela inwestycyjnego zgodnie z teorią Markowitza (1952), ale na realizowaniu długoterminowego planu inwestycyjnego zorientowanego na cele i uwzględniającego różnorodne indywidualne preferencje inwestycyjne, horyzonty czasowe i profile ryzyka danego klienta. Taki plan wymaga zbudowania znacznie bardziej zindywidualizowanego modelu, który optymalnie dostosowuje się do potrzeb danego klienta (Jung i in., 2019). Sironi (2016) zauważa, że wykorzystując osiągnięcia nowoczesnej technologii, w tym analizę big data, robodoradcy mogą oceniać, jakimi cechami charakteryzują się ich klienci w czasie, kiedy są na studiach, kiedy kupują pierwszą nieruchomość bądź kiedy przechodzą na emeryturę. ...
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Osiągnięcia technologiczne i informatyczne gospodarki 4.0 stały się motywacją do wprowadzania na rynku finansowym nowych produktów i usług finansowych oferowanych w świecie cyfrowym i odpowiadających na potrzeby zmieniającego się rynku finansowego. W ten sposób narodziły się innowacje finansowe w gospodarce 4.0. Z jednej strony dają one duże możliwości rozwoju, z drugiej są nowym wyzwaniem, z którym muszą się zmierzyć zarówno instytucje nadzorujące i organizujące ten rynek, jak i jego uczestnicy. W książce dokonano charakterystyki i oceny wybranych innowacji finansowych powstałych dzięki osiągnięciom gospodarki 4.0, której dynamiczny rozwój stał się istotnym elementem obecnych czasów. Autorzy, pracownicy Instytutu Finansów Uniwersytetu Ekonomicznego w Poznaniu, skupili się kolejno na: wykorzystaniu technologii blockchain w finansach, obrocie i opodatkowaniu kryptowalut, emisji cyfrowych tokenów, pozyskiwaniu kapitału za pomocą crowdfundingu z nagrodami, oferowaniu usług w bankowości cyfrowej i za pomocą aplikacji PFM wspierającej zarządzanie domowym budżetem, porównaniu doradcy 2.0 i robodoradcy oraz wykorzystaniu algorytmów uczenia maszynowego w budowie i zarządzaniu portfelem inwestycyjnym, a także na zmianach technologicznych w audycie finansowym. Przy tym poszukiwali odpowiedzi na pytanie o to, w którym kierunku podąża współczesny rynek finansowy działający w środowisku nowoczesnych technologii. Odpowiedź tę znaleźli dzięki opartej na najnowszej literaturze przedmiotu charakterystyce i analizie mocnych i słabych stron oraz szans i zagrożeń rozwoju różnych innowacji finansowych. Zachęcamy do sięgnięcia po tę pozycję wszystkie osoby zainteresowane problematyką gospodarki 4.0, szczególnie w aspekcie ekonomicznym i prawnym, podejmujące różnego rodzaju wyzwania związane ze współczesnymi finansami. Mamy nadzieję, że przedstawione przez nas treści pozwolą Czytelnikowi rozwinąć horyzonty i staną się inspiracją do kolejnych żywych dyskusji na temat zmieniającego się świata finansów i przyszłości gospodarki światowej.
... However Maume (2019) underlines that robo-advisory result in persisting legal uncertainties in practice and required regulatory change does not seem likely in the near future. Jung et al. (2019) underline that regulatory authorities have raised concerns for robo-advisors regarding conflict of interests, the poor assessment of risk tolerance, the missing personal contact and consequentially the unfulfilled fiduciary duty towards investors and regulatory authorities. Gurrea-Martínez and Wan (2021) discuss that regulatory challenges for the robo-advisory industry, including privacy & security and and the need for interdisciplinary cooperation for the regulation. ...
Öz Makinelerin düşünüp düşünemeyeceğini sorgulayan ilk kişi Alan Turing değildi, ancak olumlu bir yanıt veren en yakın kişi olduğu söylenebilir. Yapay zeka (AI) teknikleri, otomatik (kendi kendine) giden arabalar, karanlık fabrikalarda üretim, sohbet robotları ve robo-danışmanlar gibi birçok amaç için kullanılmaktadır. Yapay zekanın finans sektöründe artan kullanımına paralel olarak, ampirik çalışmalar da yapay zeka tekniklerinin geleneksel modellerden daha iyi performans gösterdiğini ortaya koymaktadır. Bu çalışma, Türk finans sektörü örneğinde yapay zeka tekniklerinin yatırımcıların karar verme süreçleri üzerindeki etkilerine ışık tutmayı amaçlamaktadır. Literatürde ilk olarak bu çalışma, bazı politika önerileri geliştirmek için Türkiye ve AB uygulamalarını karşılaştırarak, Türk finans sektöründeki mevcut AI politika çerçevesini ve endüstri uygulamalarını da gözden geçirmektedir.
... Profitability analysis is helpful to know the banking business life (Rajan, 2019). Increasing the profit is normally attract to the investor and it is also helpful to long term survival in the market (Schoenmaker & Schramade 2019;Jung et al., 2019). Agarwal (2019) analyze the Public and Private sector banks. ...
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The fundamental objective of this study is to acquire an understanding of the profitability of the many private sector banks that were selected for the purpose of investigation. A lottery was used as the fundamental method of random sampling for the purpose of this inquiry. The sample unit for this investigation will consist of five banks that were chosen by the researcher. The investigation will take place over the course of a period of five calendar years, commencing in 2017 and concluding in 2021. The researcher made use of a variety of accounting techniques, such as profitability ratios, as well as statistical methods, such as one-way analysis of variance; the findings are discussed further down in this section.
The purpose of this study is to assess the relationship between traditional financial advisors and the increasing use of robo-advisors, in the context of the financial technology (fintech) revolution that we are witnessing now. Contrary to the doomsday outlook of many, these services complement each other and therefore are inclusive. A growing trend, however, is the democratization of the use of fintech in an increasingly varied realms associated with personal finance. While those with higher incomes and more wealth tend to pay for the services of traditional advisors, clients with lower levels of income and wealth utilize robo-advisors more often. Continuous advancements in fintech provide a somewhat customized service but the robot’s lack of ability to manage certain types of investments and integrate intangible yet critical human factors into the assessments limit the effectiveness of these tools, at least currently.
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Motivation: The digitization of financial services has brought a noticeable decline in the importance of branch banking for customers wanting to contact their bank or receive financial services. The lack of personal contact with bank employees has become particularly significant during the pandemic, with remote modes of communication reducing the risk of contracting SARS-CoV-2. Aim: The present study applies the Spearman’s rank correlation coefficient and binomial logit model to determine the impact of the digitization of bank services and the COVID-19 pandemic on retail customers’ needs for branch banking. Results: The empirical research conducted demonstrated that the factors significantly positively influencing the need to use financial services provided by staff in bank branches were respondent experience in using bank advisory services and fear of interpersonal contact in the pandemic context. Meanwhile, variables with a negative effect were the use of the electronic banking platform, attitudes towards the use of the artificial intelligence technology in the banking sector, and perceptions of the advantages of remote communication with institutions in the pandemic context.
At the frontier of personal finance and FinTech, robo-advisors aim to provide customized portfolio strategies without human intervention. These new investment technologies typically propose passive strategies that match investor objectives and risk profiles at a low cost. This chapter explores how digital advisors lack precision in capturing clients’ attitudes towards risk and exposure. In this context, leveraging big data and artificial intelligence techniques can improve the principal strength of robo-advisors, i.e., their ability to provide automated, personalized investment solutions. Text data from dialogue systems, such as chatbots, can be employed to improve the client profile, while recommendation systems can use big data from financial social networks to recommend targeted investment strategies. Analysis of big data through machine learning methods can also improve the performance of the optimization algorithms employed by digital advisors. This chapter explores the vast potential for exploiting big data and artificial intelligence in automated asset management.
In this paper, we investigate the potential demand of robo-advisory among Millennials and the Generation Z. Using data from a survey that we designed and fielded in Italy, we show that individuals with an advanced level of financial knowledge are more likely to be potential users of robo-advisors. We also find that only online activities that entail a financial component, such as buying online and making digital payments, are predictive of being interested in the delivery of financial advice through digital platforms. On the other end, non-financial online activities are not associated with the interest in a FinTech robo-advising tool.
At the beginning of 2020, a new disease (Covid-19) has emerged and expanded swiftly all around the world. Currently, we are still living in unprecedented times in which we should have social distance from one another and mostly work from home to diminish the spreading speed of the Covid-19. While we are experiencing these limitations, businesses and organizations are expected to work without having any disruptions. At this point, entering new technologies into our lives was inevitable and in fact, these new technologies have helped us to eliminate the challenges caused by the precautions with fewer flaws. Even though Fintech applications have numerous advantages, nothing comes without its drawbacks. The empirical analysis in this chapter aims to evaluate selection of best Fintech-based investments in the Turkish banking sector with an application of a hybrid DANP-fuzzy TOPSIS technique. Empirical findings of the analysis indicate that Strategy 2 has the best rank among the alternatives, followed by Strategy 1 and Strategy 3, whilst Strategy 4 has the weakest importance among the strategy preferences. Our findings suggest that policy makers/bank managers should focus more to direct Fintech investments firstly to lending services followed by payment systems.
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Automated financial product advisors-"robo advisors"-are emerging across the financial services industry, helping consumers choose investments, banking products, and insurance policies. Robo advisors have the potential to lower the cost and increase the quality and transparency of financial advice for consumers. But they also pose significant new challenges for regulators who are accustomed to assessing human intermediaries. A welldesigned robo advisor will be honest and competent, and it will recommend only suitable products. Because humans design and implement robo advisors, however, honesty, competence, and suitability cannot simply be assumed. Moreover, robo advisors pose new scale risks that are different in kind from the risks involved in assessing the conduct of thousands of individual actors. This Essay identifies the core components of robo advisors, key questions that regulators need to be able to answer about them, and the capacities that regulators need to develop in order to answer those questions. The benefits to developing these capacities almost certainly exceed the costs, because the same returns to scale that make an automated advisor so cost-effective lead to similar returns to scale in assessing the quality of automated advisors.
We propose an experimental study to examine how to optimally design a robo-advisor for the purposes of financial risk taking. Specifically, we focus on robo-advisors which are able to (i) “speak” the language of the investors by communicating information on the statistical properties of risky assets in an intuitive way, (ii) “listen” to the investor by monitoring her emotional reactions and (iii) do both. The objectives of our study are twofold. First, we aim to understand how robo-advisors affect financial risk taking and the revisiting of investment decisions. Second, we aim to identify who is most affected by robo-advice.
Conference Paper
We propose an experimental study to examine how to optimally design a robo-advisor for the purposes of financial risk taking. Specifically, we focus on robo-advisors which are able to (i) "speak" the language of the investors by communicating information on the statistical properties of risky assets in an intuitive way, (ii) "listen" to the investor by monitoring her emotional reactions and (iii) do both. The objectives of our study are twofold. First, we aim to understand how robo-advisors affect financial risk taking and the revisiting of investment decisions. Second, we aim to identify who is most affected by robo-advice.
This book focuses on market developments of crowdfunding, crowdinvesting, crowdlending, social trading, robo-advice, personal financial management, online payment and mobile payment in Germany. FinTech companies are an important driver of innovation in the financial industry. By making financial transactions more user-friendly and transparent, these firms potentially contribute to financial stability and economic growth. The authors define and categorize the different market segments that have emerged. They further provide an assessment of current market volumes and make forecasts for the next 5, 10 and 20 years. Particular attention is given to the empirical findings resulting from scholarly research. Furthermore, the authors evaluate how the German FinTech market ranks relative to international standards. This book will appeal to finance and entrepreneurship researchers as well as practitioners from banking and tech industries. “This book offers a fresh and fascinating look at the FinTech market. The authors provide a rigorous economic analysis of the FinTech market in Germany and offer many insights that are of interest to practitioners, academics, and policymakers alike.” Professor Douglas Cumming, Schulich School of Business “Germany is one of the fastest growing FinTech markets in Europe. This book not only provides a comprehensive and systematic overview on the developments and actors, but undertakes a visionary outlook on the forthcoming decades based on scientific methods.” Dr. Thomas Puschmann, Head of Swiss FinTech Innovation Lab
Banks have reacted much more enthusiastically to the FinTech revolution than many of their customers. Robo-advisory, automated web-based investment advisory, in particular promises many advantages for both banks and customers - but consumer adoption has been slow so far. Recent studies suggest that this might be due to a mix of low trust in banks, high expectations of transparency and general inability or unwillingness to engage with investment questions. Research in decision support and guidance shows customers’ willingness to interact with a decision support tool depends greatly on its usability. We identify requirements for robo-advisory, derive design principles and evaluate them in two iterations with a real robo-advisor in a controlled laboratory study. The evaluation results confirm the validity of our identified design principles.
This book provides an introduction to the state of the art in financial technology (FinTech) and the current applications of FinTech in digital banking. It is a comprehensive guide to the various technologies, products, processes, and business models integral to the FinTech environment. Covering key definitions and characteristics, models and best practice, as well as presenting relevant case studies related to FinTech and e-Business, this book helps build a theoretical framework for future discussion.
Robo-advisors have been touted by the Department of Labor as a source of investment advice that can benefit retirement investors by minimizing costs and avoiding conflicts of interest. On the other hand, they have been labelled as gimmicky and overly simplistic by some critics who have used them. The Securities and Exchange Commission has cautioned that robo-advisors may result in investment recommendations that are based on incorrect assumptions, incomplete information, or circumstances not relevant to an individual investor. This paper examines whether robo-advisors in fact provide personal investment advice, minimize costs, and are free from conflicts of interest. It also evaluates whether robo-advisors meet a high fiduciary standard of care and act in the client’s best interest. Based on a detailed review of user agreements for three leading robo-advisors, this paper concludes that robo-advisors do not live up to the DOL’s acclaim. They are not designed for retirement accounts subject to ERISA and should be approached with caution by retail and retirement investors looking for personal investment advice.