The latest FinTech solutions allow customers to find their way around financial markets and succeed in them. This applies to different areas – from high-frequency trading to long-term investments. Today, anyone can have access to financial information, and you don’t need to possess specific knowledge to become an investor. Dedicated financial software solutions, including robo-advisors, will help you take the right steps in this sphere. How do robo-advisors work? What tasks do they perform? Can you entrust robots with asset management? We’ll tell you below.
When robo-advisors emerged and what they are needed for
Technology development and the use of AI and ML in financial management have given rise to a new trend in finance. WealthTech is a sphere uniting financial software solutions that manage private assets.
From 2016 to 2020, the market volume of WealthTech investments increased from $2.8 billion to $9.3 billion.
This surge has been caused by an increase in the number and value of transactions. People have become more financially literate and interested in investing. Today, market players turn to dedicated financial programs – robo-advisors – more and more often.
Robo-advisors are online services for investment management. They use mathematical algorithms to provide financial advice with minimal need for human intervention. This online service builds the best possible investment portfolio in terms of risk and return and allocates assets most effectively. Investors can tap into these smart advisors’ services via brokers.
Robo-advisors started to emerge between 2008-2010 in the USA, during the economic crisis аnd recession of that period. Betterment, the first smart advisor for financial management, was designed for investors. This advisor allowed them, thanks to a user-friendly and intuitive application interface, to manage passive investments and reallocate assets within the designated funds. Over time, the service got a wider range of features.
How robo-advisors function
A robo-advisor’s uniqueness lies in its personalized approach. When investors first start working with the service, a robo-advisor asks about their investment experience, the sum of investments, goals, timelines, expected revenue, and their attitude towards risks. All this data is then entered into a questionnaire. A smart questionnaire offers the user to answer the same questions, rephrasing them several times. This enables the program to receive the most precise information.
Based on this, the robo-advisor does analysis and places the client in one of the predetermined investor risk profiles. According to this classification, clients get an investment strategy that is in line with their goals. It includes investment tools, their share in a portfolio, and diversification depending on asset class. Clients of different profiles are offered strategies with different degrees of risk.
Sometimes financial experts contribute to the report generation in its final stage. They can adjust the plan depending on the client’s specific needs.
From this point, the robo-advisor tracks changes in the market and in the client’s profile to make sure the course set is correct and to introduce changes if needed.
Benefits of robo-advisors
Robo financial advisors allow companies to attract novice investors and receive detailed data on the profiles of platform users, information about their behavior, and preferences concerning trading strategies. Smart assistants give a business a competitive advantage in local markets thanks to their technological innovations.
Among the benefits of robo-advisors, we can mention the following:
1. Ease of use
Robo-advisors are useful for novice and non-professional investors who need consultancy and support. They help beginners get to know their way around investing and make the right decisions, while simple interfaces facilitate a quick start.
Having filled out a questionnaire in the beginning, clients may not need to actively participate in the investment process. There is no need for constant interaction between a system and an investor.
A robo-advisor provides clients with 24/7 access to the service, which an average broker can’t offer. Using a robo-advisor system is much cheaper than hiring a human advisor to perform the same tasks.
4. Technological effectiveness
Mathematical algorithms are capable of detecting and calculating possible case scenarios; they quickly react to market changes and make adjustments. Working schemes consistently demonstrate successful results.
Robo-advisors set up asset management processes according to the needs of a particular user. Moreover, thanks to the use of Artificial Intelligence, the system is sensitive to changes and quickly adjusts to them.
6. Attracting a new audience
Thanks to the ease of use, democratic nature, and accessibility of robo-advisors, investing becomes clear and appealing to a broader audience. People who hadn’t considered this type of activity before are actively getting involved in these financial transactions.
The imperfection of smart assistants
Together with robo-advisors’ obvious advantages and high performance, we ought to mention their drawbacks.
Although these services provide personalized settings, they still operate based on ready-made templates. If you need to resolve a more complex issue with many variables and draw a conclusion for prompt decision-making, it is recommended to reach out to a financial expert. Robo-advisors are aimed at long-term investments.
Robo-advisors don’t guarantee that clients achieve the level of profitability they have planned, and they aren’t responsible for possible losses due to change in market price levels. Even with a robo-advisor, investment involves risks.
Also, if a user fails to provide correct data while filling in a questionnaire, a smart assistant isn’t responsible for the proper assignment of the client to the risk profile.
A robo-advisor can have a limited set of features and fail to take into consideration all the wishes and particularities of the client’s profile. Companies use different approaches to financial software development. In some cases, when choosing a partner who provides FinTech solutions, you should consider whether it is possible to readjust the risk profile in the course of work.
Taking into account the pros and cons of using robo-advising, we can conclude that such services effectively resolve standard issues.
How to create the right robot
To remain competitive in wealth management, attract new investors, and maintain the loyalty of the existing ones, banks and financial institutions cooperate with banking software companies. The latter develop and implement robots that significantly improve businesses.
Companies that are considering using automated wealth management systems today can become more effective in the future.
Andersen provides FinTech consulting services and develops solutions for the financial sector that really work. We cooperate with major players in the global financial market. You can seek advice and receive full information on FinTech solutions from our experts.
Prospects of robo-advising development in WealthTech
Nowadays, digital robo-advisors use automation and mathematical algorithms to manage clients’ finances. They determine how to best allocate assets, set up an investment portfolio, and offer automated rebalancing services.
The platform performs certain financial transactions so that the client achieves the desired goal. Many steps in this process are predetermined and scenario-based. Sometimes, financial experts need to interfere in the process to make adjustments.
This is how it works today. However, a more flexible approach in the work of robo-advisors is sparking interest. Even greater personalization with consideration of the individual peculiarities of an investor, comparison of different profiles’ capabilities, and increased ability to change its methods of work – these advantages will be available in the future. Smart assistants are soon expected to take the form of personal financial advisors with a full range of services. This is possible to implement with the help of new cognitive technologies and Machine Learning.