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The financial industry has seen incremental augmentation in the use of technology in various offerings, known as FinTech. One of the latest technologies to emerge in the wealth management industry is that of robo-advisory services. Robo-advisors are gradually gaining credibility as an alternative tool for millennials who have an inclination towards investing. Investors, especially young and technologically savvy investors, consider robo advisory a viable and cost-effective means of obtaining investment advice. Robo advisors help the investor by making financial decisions with a set of computer algorithms, which can be apprehended only by a set of esoteric investors. These digital advisory services cannot undermine the existing financial advisory business. However, investors are still sceptical and seemingly interested in looking for human intervention when it comes to making financial decisions.
According to Fein (2015), robo advisors are online platforms that provide advice that is algorithm driven. The key approach of this platform is to invest in low-cost exchange traded funds (ETFs) that minimize embedded costs. This form of advice is directed towards retail investors’ investment options. There is a dearth of studies in this area, specifically when it comes to robo advising in developing countries like India. The total amount of assets under management (AUM), globally, by these digital advisory services is estimated to reach around USD $987,494 million by the end of 2020 (Statista Market Report, 2020). However, in India, this estimated amount will not even be close to its 1% by the end of 2020.
In a recent article in Business Insider, a comparison was made between robo-advisors and traditional advisory services. Robo-advisory services are always better for investors as they give them the cost advantage. Nonetheless, financial advisors have a competitive edge over robo advisors when it comes to tackling the emotional element of individual investors. Robo advisors are categorised as natural intelligence, where the decisions are programmed inside machines. Robo-advisors, however, may not be effective when it comes to large portfolios or complicated financial situations (Business Insider Market Report, 2019).
Most studies of the target market for this service have revealed that millennials are most likely to adopt this service. Primarily there are two reasons for this. First, millennials are quick to adopt new technology and its related services. Second, they would be cautious to invest, as they will not have a large corpus to invest. This is where robo advisors can help these investors in providing guidance to them.
The investors’ evaluation technique for the riskiness of equity cash flows affects the security value. This security value could arguably be the possible indicators of economic growth in the country (Harvey et al., 1999). In a way, appropriate investments by any class of investors viz. high net worth individuals (HNIs), retail investors, and institutional investors, should be focused in the right direction for achieving their desired outcomes. Moreover, retail investors’ participation is promising in all investment avenues, be it a mutual fund, stock market, fixed deposits, etc. This participation can be beneficial to retail investors, as well as to the market, provided they get the right guidance and advice.
Robo advisors are majorly oriented towards exchange traded funds (ETFs) and passive investing. ETFs are world-class assets that are not easily accessible by individual investors, but robo advisors makes this available to retail investors.
The primary role of robo advisors is to assess the risk appetites of investors, and then recommend the portfolio allocation. The perception about robo advisors primarily lies in the authenticity of its portfolio recommendation system. Robo advisors provide portfolio recommendations based on an investor’s profile. Financial social relationship networks among investors and institutions could be helpful to curb recommendation issues (Xue at al., 2018). Researchers are focusing on extensive machine learning techniques, such as incremental kernel techniques, to improve portfolio recommendation systems. This can be done with the help of auto initialising training datasets by combining different sources of information (Liu et al., 2018). Morana et al. (2018) posited that robo advisory services are an important field upon which to work.