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AI, humans and the new age of asset management

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Summary

This article discusses how artificial intelligence (AI) has revolutionized the asset management industry, with investors using AI for data processing, private market deals, and client customization. AI has also improved trade-execution algorithms, searches for new sources of alpha, and data management. However, the article also highlights the need for "guardrails" to mitigate risks and the importance of human intelligence (HI) in combination with AI for best results. AI has its limitations, and the skills of the future involve both technical and soft skills, as well as judgement and inference. Ultimately, AI + HI will deliver long-term value if risks are managed properly.

Q&As

What did Steve Jobs, Thomas Edison and Arthur C Clarke define as innovation?
Steve Jobs described innovation as “putting a ding in the universe”; Thomas Edison as “finding a better way to do things”; and science fiction writer, Arthur C Clarke as “going beyond the limits of the possible”.

How has the asset management industry been disrupted by data, technology and sustainability goals?
The asset management industry has been disrupted by the proliferation of data; advances in technology, including the widespread adoption of artificial intelligence (AI); and commitments to ambitious sustainability goals.

What benefits can artificial intelligence bring to asset management?
The benefits of artificial intelligence to the asset management industry include processing unstructured ESG data from alternative sources with the aim of assessing company risk; using AI in private markets to source deals and conduct due diligence; improved customisation of products and client experiences; improved trade-execution algorithms; searches for new sources of alpha through alternative data and the generation of synthetic data points and scenarios; and reduced costs for data management.

What challenges does AI present to the asset management industry?
The challenge for the asset management industry is how to make sense of the data, while providing benefits for its stakeholders, and the development of generative AI has caused divisions between optimists, pessimists, and those in-between.

How can the powerful combination of AI and human intelligence (HI) deliver long-term value?
The powerful combination of AI and HI can deliver long-term value by enabling better decisions quickly and more consistently, with the human touch.

AI Comments

👍 This article offers a comprehensive overview of the current revolution in asset management driven by the proliferation of data, advances in technology, and the use of Artificial Intelligence. It is inspiring to see the potential for AI+HI to deliver long-term value and make better decisions quickly and more consistently.

👎 This article fails to address the potential risks and ethical issues of using AI in asset management. It also does not provide concrete solutions to mitigate these risks and optimize the use of AI.

AI Discussion

Me: It's about the implications of artificial intelligence in the asset management industry. It looks at the positives and negatives, and how a combination of AI and human intelligence is needed for the industry to thrive.

Friend: Interesting. What are some of the implications?

Me: Well, AI can be used to process unstructured data and find new sources of alpha, while also reducing costs for data management. But it can also lead to the spread of misinformation and the displacement of certain jobs. So it's important to have guardrails in place and to make sure that human intelligence is still a part of the process. It's also important to remember that AI can't replicate all of the nuances of human behavior and that soft skills, like empathy and leadership, are still essential.

Action items

Research the current applications of AI in the asset management industry and explore potential use cases for AI in the future.
Develop a strategy for integrating AI into asset management processes and operations.
Investigate the potential risks associated with AI and develop a plan for mitigating those risks.

Technical terms

AI (Artificial Intelligence): AI is a type of computer technology that is designed to mimic human intelligence and behavior. It is used to automate tasks, analyze data, and make decisions.
Data: Data is information that is collected, stored, and analyzed to gain insights and make decisions.
Natural Language Processing: Natural language processing (NLP) is a type of artificial intelligence technology that enables computers to understand and interpret human language.
Image Recognition: Image recognition is a type of artificial intelligence technology that enables computers to identify and classify objects in images.
Machine Learning: Machine learning is a type of artificial intelligence technology that enables computers to learn from data and make predictions.
ESG Data: ESG data is environmental, social, and governance data that is used to assess the sustainability of a company.
Private Markets: Private markets are markets that are not open to the public and are typically only accessible to accredited investors.
Due Diligence: Due diligence is the process of researching and verifying information about a company or investment before making a decision.
Algorithms: Algorithms are sets of instructions that are used to solve problems or complete tasks.
Alpha: Alpha is a measure of an investment's performance relative to a benchmark index.
Synthetic Data: Synthetic data is data that is generated by artificial intelligence algorithms.
T-Shaped Skills: T-shaped skills are a combination of technical skills and soft skills.
Situational Fluency: Situational fluency is the ability to quickly assess a situation and make decisions.