Is it time to embrace the algorithm and let the robots take the wheel? A new breed of wealth management companies has gained traction over the last few years. Called robo-advisors, these companies ask their prospective investors numerous questions to understand the investor's attitude towards risk, loss tolerance, personal tax situation; then they combine that information with investment management models and build a personalised investment portfolio that takes the emotion out of investing—providing consistent, data-driven decisions.
According to a report compiled by Statista, assets under management in the Robo-Advisors market are projected to reach US$1,802bn in 2024 and show an annual growth rate (CAGR 2024–2028) of 6.68% resulting in a projected total amount of US$2,334bn by 2028. This poses a question of trust: Can people rely on a team of robots to manage finances, or is there still something irreplaceable about the human touch?
Building a Robo Advisory Platform
In 2019, Fabio Dias, a former senior banker and currently the lead instructor for financial modeling at the University of Surrey, noticed a growing gap in the investment advisory market. Many of his friends and family members, who were not wealthy enough to afford traditional financial advisors, struggled to manage their investments effectively. This observation sparked the idea for an automated investment service that could democratize financial advice.
With a dual background in finance and computer science, Dias spent countless hours researching algorithmic trading, portfolio management theories, and the evolving landscape of fintech. He began developing a prototype for what would eventually become a robo-advisory platform, aiming to provide accessible investment solutions for individuals with varying levels of financial knowledge and capital.
Dias faced numerous challenges in the initial stages. Building a reliable and secure platform required assembling a team of experts in both finance and technology. "Behind a team of robots, you must have a team of expert humans," said Dias. He worked tirelessly to navigate regulatory requirements, ensuring the platform complied with all financial regulations to build trust with potential users. After several years of development, testing, and refining the platform, he launched his investment portfolio service, aiming to transform the way people manage their investments by combining financial acumen with technological innovation.
Known as Stalwart Holdings, the company uses algorithms to understand the client's attitude towards investment risk, analyze available data, identify patterns, and make informed investment decisions. Today, the company has clients in Brazil and in the United Kingdom.
Human Intuition vs. Machine Precision
Human intuition has long been a foundation of investment management, with seasoned professionals relying on experience and gut feelings to deal with markets. AI challenges this norm by offering precision and consistency that human judgment often lacks. AI-driven models can process and analyze data at remarkable speeds, making data-driven decisions free from biases.
Despite these advantages, human intuition undeniably still holds value. Market conditions can change quickly, and unforeseen events may require the understanding that only human experience can provide. While AI excels in pattern recognition and data analysis, it cannot creatively adapt to novel situations.
With this in mind, a balanced strategy that combines human intuition with machine precision might be the optimal strategy for investment management. "The use of artificial intelligence helps to scale, but it will never eliminate the need for human experts behind the machines," said Dias.
Transparency in the Age of Algorithmic Trading: A Critical Examination
Trust is the basic building block of financial services, and pure AI isn't an enabler of trust. A recent study by the Washington State University found that using the term 'artificial intelligence' in product descriptions reduces purchase intentions by consumers, with the effect being even more pronounced in high-stake areas such as medical devices or financial services.
As AI-driven algorithmic trading becomes more common, guaranteeing transparency in these systems becomes a non-negotiable requirement. "Investors need to understand how decisions are made, what data is used, and how algorithms are designed to manage risks," shares Dias.
That said, achieving transparency in AI systems still comes with challenges. Some algorithms can make it difficult for even experts to comprehend their inner workings, and this opacity can lead to mistrust and skepticism among investors. "The industry must prioritize the development of explainable AI models and establish robust regulatory frameworks to ensure transparency and accountability," adds Dias.
Mr Luiz Paulo Brasizza, former investment director at the pension scheme of Volkswagen do Brasil, believes that "the big question to be answered by Fabio Dias is linked more to the compliance of the operations of a fund managed by artificial intelligence than to the investment decision itself. The key point will be the structuring of an algorithm that is transparent to the financial market so that investors, especially new generations, can be sure that the fund works in line with their aspirations."
"If, on the one hand, with AI we will have the need for greater investor identification with the fund, I understand that on the other hand we will also have the need for increasingly complex due diligence processes with the managers. In my opinion, only a manager who continuously and consistently applies high ethical standards and transparency in processes will be able to survive in this new world," said Brasizza.
Accountability and Responsibility
The implications of automated investing are noteworthy. While powerful, AI systems are only as good as the data they are trained on and the objectives they are designed to achieve. Programming these systems to operate ethically and responsibly is needed, so investment managers must address potential biases in their algorithms so AI models do not perpetuate harmful practices.
Accountability is another critical aspect. Determining responsibility can be challenging in a scenario where an AI-driven system makes a poor investment decision. According to Dias, "establishing clear guidelines and accountability measures will be essential as AI continues to play a bigger role in investment management."
AI-powered systems must also be equipped to deal with this market volatility. AI models excel in stable conditions where historical data can accurately predict future trends. However, they may struggle to adapt during extreme volatility or large-scale events.
Companies like Stalwart Holdings address this issue by managing portfolios using derivatives such as futures and options. "The ability of AI to process real-time data and make rapid adjustments can be advantageous during volatile periods. That said, the industry must continue to develop AI systems capable of handling market unpredictability to give consistent performance," shares Dias.
Combining Academia and AI
Whether investors should entrust their money to a team of robots is a complex and multifaceted debate. AI-driven investment management truly offers significant advantages in terms of efficiency. However, it also presents challenges regarding transparency, ethics, and adaptability.
Stalwart Holdings and similar companies are proving AI's potential while addressing its inherent risks. Imagine never worrying about panic-selling during a market dip or missing out on financial gains because of fear. With robo-advisors, portfolios could be one step ahead of the game. They also do not need coffee breaks or vacations, making them the ultimate 24/7 financial guardians.
