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Machines vs people. Investment management perspective

Will AI and robots become too smart to replace humans in the future? This fear is increasingly coexisting in the human mind, and has penetrated into discussions about FinTech (1).

We believe in the power of the “AI + HI” model (artificial intelligence + human intelligence), that is, most tasks are best solved and will be solved using both AI and human intelligence, and the collective strength of these two elements surpasses any of these elements in separately (2).

AI adoption starts with rudimentary tasks such as gathering information from text and images, creating reports and populating spreadsheet models, where AI has some advantage over humans in the amount of information it can process at high speed. After that, analysts can perform more important tasks that require more experience and judgment.

This is not a race between people and cars. Ultimately, competition is between AI + HI teams, and stronger teams that leverage and combine both elements effectively will drive out weaker teams. Successful investment teams of the future will have collective intelligence through cognitive diversity (artificial and human) and T-shaped skills.

Prospects for the use of AI and big data in investment management

Despite the important role AI and big data play in the investment industry, they are not a panacea. In some situations, the alpha channel provides additional information, and in other situations, extended algorithms (ML) can detect new patterns. Yet AI and big data will definitely not provide all the answers that investors need.

For example, one of the challenges faced by machine learning methods is that they perform better in a test environment (based on a training dataset) and may not always respond correctly to new situations in the real world.

This is an overfitting problem where algorithms perform well on sample but poorly out of sample. AI can run on AlphaGo where all the rules are set. However, the investment world is constantly changing, which presents more difficulties. Other than that, some of the business machine learning programs are more like a black box; users do not have access to the logic of machine learning actions. As a result, some of the functions captured by the programs do not have a causal relationship to the variables that the algorithm models try to predict.

In the future, as technology advances and understanding expands, these challenges can be overcome, but for now, we must put the power of AI and big data in perspective as we embark on a journey of exploring the unknown.


Based on our research, interviews and conversations with scientists, as well as investment and technology practitioners, we concluded:

1. Artificial Intelligence and Big Data can lead to some of the most significant changes in the investment management industry that today’s professionals will experience in their careers.

2. Successful investment companies are already planning a strategy for integrating artificial intelligence and big data technologies into their investment processes right now.

3. Successful investment professionals recognize and capitalize on the opportunities offered by new technologies and applications through collaborative organizational culture, cognitive diversity, and T-shaped teams.

(1) See, for example, and

(2) See Larry Cao, Fintech 2018: The Asia Pacific Edition (Charlottesville, VA: CFA Institute, 2018) and CFA Institute, Investment Professional of the Future (Charlottesville, VA: CFA Institute, 2019).

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