Artificial intelligence created enormous efficiencies in many industries but has yet to in the financial world. In his article for finews.first, Martin Velten expresses his conviction the possibilities of AI-driven asset management are so great there'll be no way around it in the future.
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Until now, wealthy bank customers relied on advisors. The advent of artificial intelligence (AI) is opening up a new world in wealth management as well, one that can be fully tailored to the needs of individual clients.
In other sectors of the economy, AI has already created incredible efficiencies, something that's rarely been the case in the financial industry. Because AI-driven wealth management is such a new field, it's constantly evolving. AI can be used in asset management in two ways: first, to make investment recommendations, and second, to automate investment tasks.
When it comes to investment recommendations, AI can be used to collect and analyze financial data or make predictions about market trends. This information can then be used to create investment recommendations tailored to the individual client. An AI system can recommend investing in certain asset classes or industries if it predicts they will outperform in the near future.
«There are several differences between AI-driven asset management and traditional asset management»
AI can be used to automate investment management tasks in portfolio management, including portfolio rebalancing, trade execution, and risk management. AI can help relieve the burden on human advisors, allowing them to focus more on serving their clients and improving business processes. In some cases, it can be used to develop new products more quickly and adapt business models to changing demands.
There are several differences between AI-driven asset management and traditional asset management. AI-driven asset management is much more data-driven than traditional asset management, the latter using conventional methods, which require human interaction. In contrast, AI systems can use their algorithms to collect and process large amounts of data more quickly and effectively.
This capability provides AI-based systems with a competitive edge due to higher accuracy in making market predictions. This leads to better risk management and therefore higher returns over a long-term investment horizon.
«AI-based asset managers tend to be more cost-efficient than their traditional counterparts»
Another difference in AI-driven wealth management is that it's highly personalized. Machine learning and AI systems analyze the data of individual clients and create a suitable investment strategy tailored specifically to that person's needs. In contrast, traditional portfolio management approaches tend to be much more general with their fundamental analysis, trading strategies, and investment strategies.
In addition, AI-based asset managers tend to be more cost-efficient than their traditional counterparts. That's because their lower fees are ultimately the result of their ability to process large amounts of data more efficiently and rely on machine-learning algorithms rather than human research and performance monitoring, which increases the cost of investment advice at traditional firms.
«However, there are still some challenges as well»
Finally, the use of AI enables a more systematic and consistent approach to investment decisions. This helps to achieve higher returns and grow investment portfolios more efficiently. With AI-based asset management, sophisticated algorithms can be relied upon to make informed investment decisions based on market trends and data analysis.
But several challenges also remain for AI technology-driven platforms to be truly successful. First, these systems must be able to store large amounts of data, clean up errors, and process the data. This is not always easy, as the data used to make investment decisions is scattered across a wide variety of sources, including news articles, financial reports, and corporate documents.
«AI systems can therefore only be as accurate as the data they are fed»
Another challenge is that AI systems are expected to generate accurate predictions about market developments. This is a difficult task because the future is inherently unpredictable. AI systems can therefore only be as accurate as the data they are fed; even small errors in the data can lead to inaccurate predictions.
Finally, it's very important that AI-driven asset managers explain their predictions to clients and fully communicate the benefits of the technology to them. This is important in that investors should understand why the AI-powered asset manager recommends, for example, selling stocks or buying securities in a company or industry to generate a particular course of action.
Last but not least, AI companies must be able to generate investment recommendations specifically tailored to the individual client. This is a huge challenge because each person's financial situation is unique. What works for one person may not work for another simply for regulatory reasons.
Martin Velten has been a partner at Zurich-based digital asset manager Smart Wealth since the beginning of 2021. As Chief Operating Officer, he's responsible for sales and servicing fund managers, banks, family offices, advisory platforms, and other professional investors. For three decades, he worked in capital markets at major banks, including Commerzbank, Unicredit, and Deka Bank. He's developed numerous product innovations and trading areas, including the first certificates and guarantee products. He's considered a pioneer of the European ETF industry.
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