In May 2022, Allan Sloan, a seven-time winner of the Loeb Award, published the article 'The democratization of investing': Index funds officially overtake active managers, highlighting that for the first time in history, retail investors’ index fund holdings exceed their holdings in actively-managed funds. Outperforming the benchmark, or finding alpha, has been the main purpose of all active managers. However, active managers find it increasingly difficult to beat the indexes. The main reason for weak performance of active managers is competition. As the industry grows, so does the competition, particularly from new funds. Therefore it becomes harder to outperform in a bigger and more competitive market.
While Wall Street Investment Banks such as BlackRock have been investing in Artificial Intelligence (AI) for years, active asset managers across all fund sizes are turning to AI to improve their results. BlackRock got into the SaaS business more than 20 years ago. Other institutional asset managers including State Street, Pimco and Amundi only recently started to heavily invest in AI investment platforms, hoping to get a slice of BlackRock’s AI business, which is generating new revenue even as markets are falling.
There are several reasons why asset management firms are using AI today. One reason is that AI can help to improve the efficiency and accuracy of investment decision making. By using machine learning algorithms, asset managers can quickly analyze large amounts of data and identify trends and patterns that might not be easily apparent to human analysts. This can help asset managers to make more informed investment decisions, which can ultimately lead to better returns for their clients.
Another reason why asset management firms are using AI is that it can help them to better understand their clients' needs and preferences. By using AI to analyze data from a variety of sources, asset managers can gain a more comprehensive view of their clients' investment goals and risk tolerances. This can help them to tailor their investment recommendations and strategies to better suit the individual needs of their clients.
Furthermore, AI can help asset managers to automate certain tasks and processes, such as portfolio rebalancing and risk management. This can free up time and resources that can be used to focus on more value-added activities, such as providing personalized investment advice to clients.
Overall, the use of AI in asset management can help firms to improve their investment decision making, better understand their clients, and automate certain tasks and processes, which can ultimately lead to better outcomes for both the firm and its clients.
These last four paragraphs state the obvious when it comes to AI in today’s asset management. In fact, all four paragraphs have been generated by AI by asking ChatGPT why asset managers today need AI. However, AI might improve the results of active managers only temporarily, as new strategies are being copied and many more players in the market start using AI solutions. This is what S.P. Kothari, Gordon Y Billard Professor of Accounting and Finance, and Robert C. Pozen, Senior Lecturer of Technological Innovation, Entrepreneurship, and Strategic Management, MIT Sloan School of Management described as a paradox in a recent article in International Banker: active managers will have to devote substantial resources to AI and ML to keep up with the pack. In the words of Kothari and Pozen: “To remain competitive, it is imperative that asset managers arm themselves with ML to guide their portfolio construction and trading decisions. However, in a competitive market, the benefits from ML are likely to be copied and commoditized—i.e., they are unlikely to boost their investment performance on a permanent basis.” Kothari and Pozen come to the conclusion that customization of services is the only way active managers can outshine the competition in a playing field that is increasingly leveled by AI.
Performance is a key attribute for clients when selecting an investment advisor. However, there are other attributes actively increasing the quality of the overall service such as real-time customization of portfolios based on the preferences of clients. For many big players personalization has become a top priority. Ralph Hamers, CEO of UBS, recently stated that “clients today are used to things being personalized, relevant, on-time and seamless, similar to their experience with platform companies” and that investment advisors need to be supported by data-driven AI systems to deliver relevant advice in time.
As the asset management business is increasingly becoming a technology business, the wealthtech market is set for substantial growth and expansion behind the momentum of cutting-edge tech. Grand View Research, a California-based research firm, expects that the market growth will be driven by an "increasing demand for wealth management software from financial advisors to effectively understand the needs of their clients and streamline the financial management of their clients accordingly." When discussing the rise in asset managers turning to AI-backed applications to offer personalized products to their clients, Grand View Research says businesses are particularly adopting predictive analytics tools based on AI and machine learning to analyze the large volumes of data related to investments and forecast future trends. The study further explains the drivers of the trend: "The increasing number of high net worth individuals across the globe is expected to play a niche role in driving product adoption over the forecast period."
Today, asset management is poised to leverage the power of personalization, cloud-based infrastructure and real-time AI to drive growth similar to retail-oriented platforms. E-commerce platforms have shown that personalization is a proven approach to engage users and drive positive outcomes. Offering a cloud-based AI engine that is designed to deliver personalized asset management products, we at Aisot Technologies AG invite asset management firms to partner with us and harness the power of AI for better and more personalized client outcomes.