Artificial intelligence (AI) used in asset management provides scenario forecasting, portfolio optimization, and investment process automation, aiming to enhance return efficiency and mitigate risk.
Interest in artificial intelligence has grown exponentially in recent years, and machine learning is often described as a process that uses mathematical data models to enable a computer to “learn” without the need for explicit programming.
The adoption of AI is not new in the asset management industry, although it has developed significantly in recent years. The main players are in the investment fund industry, offering quantitative solutions to their clients.
Despite technological advances and the productivity gains resulting from the use of artificial intelligence, we must, however, remain aware of its limitations and the risks that certain aspects of portfolio management may entail.
Currently, asset management remains a domain where artificial intelligence faces significant challenges, particularly in management roles and in the realm of personal and business relationships. The inherently human nature of the activity, focused on building trust and maintaining ongoing interaction between the client and the management professional, makes the complete replacement of human capacity by artificial intelligence complex. These challenges are evident, on one hand, in the technical aspects of asset management and, on the other, in the essential need to understand and manage interpersonal relationships – areas that continue to resist automation created by artificial intelligence.
