Data Is The Future of Asset Management: But It Comes with a Catch

Irrespective of the industry, AI and ML are now all the rage, and asset management is no exception. By 2027, around 16% of asset managers will supposedly disappear due to a paradigm shift in technological advancements and investor expectations. AI and ML technologies are being used in various aspects of the financial industry. It’s all about adopting a data-driven approach instead of the traditional way in which asset management has been taking place for many decades.

There is no doubt that AI tools and big data can positively impact asset management and make it more efficient. But it is definitely not the answer to all your wealth management problems. For starters, data is still regarded as a raw material that can aid in decision-making. It’s not yet an asset or a strategic tool that is clearly linked to the desired outcome. To truly integrate the data-driven approach into asset management, companies need to delve deeper and look for ways to use data in an all-pervasive way.

Tools Alone Cannot Get the Job Done

One of the biggest problems of automating tasks and processes is that most companies tend to make these decisions in a vacuum. This is a classic example of “following the herd.” Implementing automation just because everyone else is doing it isn’t going to give you a competitive edge. In fact, this can lead to more problems than one can imagine. 

The asset management sector has been following a specific style of operation for decades, where market performance has been the biggest revenue driver. To make a switch to a completely data-driven approach, it is instrumental to have skilled personnel who are aware of how to use this data effectively and integrate it into the existing systems.

Instead of adopting AI and ML tools just for the sake of it, asset management companies need to embrace a scientific approach to create an appropriate strategy. Scientific basis should form the foundation for identifying market trends and evaluating customer needs. Tools can always be built based on such hypotheses and findings, but there is a need for skilled teams to navigate these tools and improvise accordingly. After all, if the teams operating the tools are not aware of their scope, the whole purpose of enhancing the asset management system is defeated. This leads us to the next point—the human factor.

A Human Touch Is Needed

The synergy between human expertise and a scientific approach is the perfect recipe to adopt AI and ML effectively in the asset management sector. Asset management often involves complex decision-making that extends beyond quantitative data analysis and might require considering qualitative factors, understanding market dynamics, and interpreting geopolitical and economic events. 

While tools like ChatGPT can swiftly produce a set of results, they aren’t a match for an efficient human approach or insights from skilled professionals. This is particularly noteworthy given the constraints of this AI tool's knowledge, still “frozen” in 2021 and unable to offer current information. The basic principles and structure of the financial sector have remained unchanged for a long time, and it will likely stay the same in the near future. A human touch of experienced asset managers will ensure personalized service and safeguard profits for the clients.

Small Data Should Not Be Ignored

With big data grabbing the spotlight in the context of technological advancements, it is essential to remember the importance of small data in the asset management sector. While big data is considered to be crucial for training AI and ML tools, small datasets and specific client stories are often the origins of most successful asset management strategies. When a certain tailored approach is successful, it is further tested and refined with a larger pool of clients. Eventually, these human-centric and insightful strategies can be scaled to meet the needs of diverse clients, irrespective of their volume of business.

AI and ML have the potential to greatly enhance asset management, but in practice, companies need to adopt a combination of human expertise and AI/ML tools. AI and ML can handle data analysis, pattern recognition, and some aspects of decision support, allowing humans to focus on higher-level strategic planning and decision-making.

Having said that, we cannot ignore that the role of humans in asset management is also evolving. As AI and ML technologies keep on developing, asset managers are increasingly becoming "augmented" by these tools, using them to enhance their decision-making capabilities—in predictive analytics, algorithmic trading, risk management, and more. This augmenting does not always have to lead to replacement. The symbiotic relationship between human judgement and machine intelligence is likely to be the future of asset management, as it leverages the strengths of both to create tailored strategies and achieve better outcomes.