5 CHAPTER II LITERATURE REVIEW 2.1 Introduction to Robo-Advisors 2.1.1 Definition "Robo-advisors" are an example of the kind of automated, algorithm-driven financial planner and portfolio manager symbolizing progress and disintermediation in the financial services sector (Tan, 2020). It is linked to the fact that these offered, for the first time, much cheaper solutions to the advisory service, which is conventionally looked at as being far too costly by the neediest target groups, as Fisch, Laboure, and Turner (2019) explain. The development of the Robo-Advisors landscape is underlined with very clear tendencies. They turn the behavior of the investor and generically the whole framework of financial advisory upside down, bringing an automated, though very much personalized, experience of advisory. The latest technologies used include the digital twin integrated with natural language processing and more with the motive of innovating to bring a change with improved standards in personalization and effectiveness in service delivery at a world level (Bonelli and Sipahi Döngül, 2023). This speaks to changes and dynamics in the way simple automated advisors were developing into far more sophisticated hybrid models, combining efficiency from algorithms with human insight and aiming to democratize the market for financial advice throughout the world. Building and being used all across the globe, robo-advisors will change the very fabric of financial advisories and signal innovation and inclusiveness in the industry. 2.1.2 The technology behind robo-advisors Technologies supporting Robo-Advisors come under the foundation of some algorithms, artificial intelligence, and machine learning. All such become part of bringing highly efficient and personalized financial advice and management of the client's portfolios. These robo-advisors leverage AI in the analysis of big data and prediction of outcomes, eventually giving the user advice tailored according to his profile on the risks and aims of investing. The machine learning part ensures that the robo-advisors can keep learning from 6 the new set of data coming under the AI domain, and hence over time, it gets more and more improved in making better recommendations. For example, Belanche et al. (2019) in a study express the following way on how such influences of AI are gathering influence in FinTech: "Robo-advisors are going to be the accelerators that speed up and further penetrate consumer behavior towards these automated advisory services." Shanmuganathan (2020) pointed out that AI applications change financial advisory services, particularly those offered by algorithms accounting for behavioral finance; he described the capability of AI to be used in creating viable, particularly customized investment portfolios for the behavior of the investors. Moreover, with the involvement of advanced AI, deep learning, and other technologies, it could even be possible to have robo-advisors developed in such a way that they provide pin-point advice on any topic related to finance and are dynamic for change, hence relevant in giving advice on investment and serving the purpose. 2.2 Adoption of Robo-Advisors With the number of internet users across the world on the rise, one would definitely expect the increased application of Robo-Advisors across countries. In fact, the pace at which the increased use of Robo-Advisors is manifesting itself is very astonishing. The total number of people using RAs in the whole world was approximately 2 million in 2017. This statistic will keep rising, with projections that in 2023, it will be at a rate of over 30 million people, joining at a 1400% rate of growth within just six years (Statista, 2023). It is estimated that come 2027, this figure is set to soar even higher, up to the total Robo-Advisors users standing at a towering figure of about 34 million. On the other hand, the Assets under Management (AUM) by RAs have exhibited remarkable growth. From a world total of 1 billion USD in 2017, AUM has grown to over 1 trillion USD by 2023—approximately 99900% growth within six years (Statista, 2023). These figures underline fast, all-encompassing marketplace development and adoption of Robo-Advisors technology worldwide. Since this research has Indonesia, a developing country, as the specific location of the research. It is needed to compare the adoption of Robo-Advisors in developed countries with the emerging markets countries. Investigations have further proof that Robo-Advisors 7 will bring down financial markets over the forthcoming decade. Penetration is more expected in emerging markets than in developed states. This is explained by the perception of "already well covered enough" in developed markets, while "untapped potential in emerging markets" leads to fertile ground for the proliferation of RAs (Tiberius et al., 2022). The article emphasizes that the trajectory of the white label branding of the Robo-Advisors services being provided by technology developers is on its way to being branded and marketed by some of the established financial institutions. That is, this B2B2C model is expected to become preeminent in the landscape; banks and financial firms enable these tools that would otherwise help improve the competitive edge through proprietary investment strategies (Tiberius et al., 2022). Such a move towards white labeled RAs, coupled with that of strategic investment by traditional financial entities into external startups, creates a very challenging environment for smaller, independent firms because of the financial stability and huge customer base of such larger competitors (Tiberius et al., 2022). 2.3 Impact on Personal Financial Planning Robo-Advisors opens the gates for wider ambit opportunities in the financial consultancy and investment sector, which has been characterized by the price sensitivity with low investment. Flexibility ensures registration and investment even with meager capital, hence opening up to a much larger range and base of potential investors, amongst which even the smaller-budget ones were not on the radar for traditional consulting firms (Waliszewski and Zięba-Szklarska, 2020). The fee structure is usually low and regressive, thus appealing to more extensive-sized investments. The other major benefit would be the possibility of being able to recover any tax loss in a jurisdiction where the difference in tax rate on short- term and long-term capital gains is different, hence providing more value to long-term investors (Waliszewski and Zięba-Szklarska, 2020). In the current era, today's Robo- Advisors platforms derive an appropriate investment strategy based on the collection of massive data and machine learning. This digital and transparent nature of platforms serves in making the right decisions and maintaining investors' confidence. Waliszewski and Zięba- Szklarska (2020) emphasize that Robo-Advisors doesn't only reduce emotional decision- 8 making, but also makes use of algorithms to keep the risk level consistent even if there are fluctuations in the market Sidat and Matchaba-Hove (2020) explore factors influencing the intentions of financial planners to adopt Robo-advisors. According to them, Robo-Advisors not only provide benefits to independent investors, but also to human financial planners because Robo-Advisors will reduce the workload of a human financial planner in carrying out his duties for his clients. RAs can do this by doing human financial planner work that is not related to client relationships. such as data collection and organizing, analyzing portfolios and delivering advice (Financial Planning Services Board, 2016). In this way, Robo-Advisors will also help in reducing the financial costs of a human financial planner. So, here it can be concluded that this does not mean that the emergence and development of Robo-Advisors will destroy the work of human financial planners. However, Robo-Advisors will be a very useful tool for human financial planners. Rossi and Utkus (2020) discuss that the characteristic of maximizing portfolio returns from Robo-Advisors makes many human-advised individual investors move to use Robo- Advisors. It can be concluded that individual investors who are aware of the benefits provided by Robo-Advisors for their personal financial planning and think about using Robo-Advisors instead of a human financial planner will increase over time (Rossi and Utkus, 2020). It is, however, worthy to underline that Robo-Advisors platforms have been criticized for not offering real cuts in investment costs while advertising that they charge low or even no fees. Often, fees are hidden behind lower returns or even increase after an interim period. The FINRA (2016) report points out that the Robo-Advisors may not entirely eliminate the conflicts of interest inherent to financial consulting, as they will often maintain relationships with related entities that could lead to biased advice. On the other hand, the report continues to underscore that the Robo-Advisors system rather poorly assesses the risk tolerance of the investor. It uses poorly devised questionnaires that can't capture the individual goals and circumstances an investor might be in.