Abstract
As the investment landscape evolves, the integration of artificial intelligence (AI) technologies with human cognitive processes emerges as the next frontier in investments.
The applications of AI in investments are extensive, ranging from research and responsible investment integration to data enhancement and investment communication. For example, machine learning models can enhance data management by reducing noise, ensuring that extensive data is not only usable but also relevant to investment processes1. Moreover, AI technologies can mitigate decision-making biases and highlight hidden risks.
A further benefit of this transformative technology is its potential to foster creativity. For example, within the research function, large language models (e.g., ChatGPT) can help create new sources of data, generate proprietary insights and inspire new research in fields, such as geopolitics, economic cycles and responsible investment. Ultimately, AI may enhance the overall investment expertise, paving the way for more informed investment decisions.
This new frontier demands that asset managers develop a distinct AI identity. To do so, it is critical that they cultivate engagement across their organisations to ensure a full understanding of the technology's capabilities, and its potential to enhance both investment decision-making and the client experience. The expected benefits of AI can be summarised as follows:
- Improving data management: Unlocks previously unmanageable data and reduces noise.
- Cultivating AI identity: A deep engagement from all teams can generate a competitive advantage.
- Enhancing investment expertise: Enables faster response to changing markets.
- Sharing knowledge: Unifies knowledge and encourages collaboration across teams.
Transforming research and insight experience: AI is inspiring new research processes and insights.
We at Amundi are focused on building an infrastructure that is tailored to our needs, working with knowledge developed internally while using AI to promote innovation, especially in the research field. However, on a cautious note, deep AI integration requires a high level of attention to data quality, potential manipulations and herd behaviours that could lead to systemic risk if many market participants use the same approaches and data. Thus, it is both unlikely and undesirable for AI to replace human-led final investment decisions.
Overall, we believe that the integration of AI will present a transformative opportunity to enhance knowledge management and develop actionable insights, enabling clients to navigate the complexities of financial markets more effectively as AI capabilities increase.
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