How AI is Transforming Investment Decision Making? | AI Use Cases

Preeti Karna Preeti Karna
Mar 14, 2022 4 min read
How AI is Transforming Investment Decision Making? | AI Use Cases
The article is contributed by Andesh Bhatti - Β Angel Investor & Founder of Collectcent.

The investment management sector is witnessing what is perhaps its most volatile moment in history. The investment landscape has changed and changed for good at that.

Investment is no longer an exclusive habit of the rich and powerful, but rather one that's becoming more widely available as new disruptive technologies make it more and more accessible to the general public. In fact, investment opportunities can now be taken advantage of with the tap of a smartphone because of the rise in demand for digitally facilitated, easy-to-understand financial services.

Although all these new technologies have the potential to revolutionise and improve the investment process, artificial intelligence (AI) is the one that offers the most potential. The technology encompasses a wide range of techniques for simulating human-like intelligence on a machine. For investors who have until now ventured on the precarious investment terrain relying primarily on their gut instincts and personal assessments, artificial intelligence offers a whole slew of opportunities.

Gartner predicts that by 2025, artificial intelligence (AI) and data analytics will be used to inform more than 75% of venture capital (VC) and early-stage investor assessments.

The Inner Voice Conundrum and Its AI Resolution

Investors who succeed in their ventures are often believed to have a sharp intuition. That’s because their capacity to make financial decisions is based on largely qualitative data like management expertise, industry cycles, strength of research and development, and labor relations; only after that is it abetted by the quantitative data provided by the financial specifics of any business. However, it’s difficult to measure an inner voice, especially when that voice is developed largely through personal experience. And, of course, it also does not give a guarantee of success. Consequently, the role it plays in investors taking financial decisions is decreasing.

The AI Answer

Rising data analysis capabilities are fast directing early-stage investing strategies away from personal judgment and qualitative decision making and toward a more sophisticated quantitative process. Data from websites like LinkedIn, Crunchbase, and Glassdoor, as well as third-party data marketplaces, will be a big part of this process. They are already giving rise to sophisticated models that can better identify the feasibility, proposal, and prospective outcome of an investment. The result being that questions like when to invest, where to invest, and how much to invest are on the verge of becoming practically automatic.

But that’s on optimizing the quantitative process alone. AI is also enabling the metrics for measuring success based on qualitative factors.

AI technology is renowned for its capabilities of predicting future behaviour and delivering insights into client preferences. Natural language processing AI that can discern features about an individual from real-time or audio recordings can further be used to generate unique profiles, now with barely any human assistance. Based on job history, field experience, and previous business performance, AI algorithms can soon be utilised to estimate the likelihood of investment success reliant on individuals.


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The Use Cases for AI in Investment Management

AI can be used in three stages of the investment decision making:

Pre-trade

To find and analyse investment opportunities, analysts devote a large amount of time to gathering, sorting, and organising relevant data. Consequently, a substantial part of their efforts is spent on data that is later found to be of little value.

Natural language processing (NLP) AI can handle a big part of this job, as it can take in large amounts of data from multiple sources, scan for trends and patterns, and then assign a score to each relationship it uncovers. Using these tools can significantly minimise the amount of time analysts spend in this phase, allowing them to focus on data that has the greatest potential for better discoveries.

At the moment of truth

Although it is up to the investor to make buying, selling, or holding choices, NLP AIs can assist in this. Relying on AI results blind sightedly is not what most investors are going to buy into. NLP can help explain the drivers of an AI decision engine and give an unbiased report that explains the decision in detail, including all the countervailing elements. This can further enable managers to deep analyse a trade and approve or reject it.

Post-investment

NLP engines can leverage structured data inputs to create performance attribution reports and periodic investor reviews. The technology has the potential to increase the speed, precision, and cost of creating reports based on the performance and strategy of the investments made.

Conclusion

The use of AI by investment managers is quick reaching a point where it could provide a competitive edge for a long time to come, by enabling better investment possibilities as well as increased operational efficiency. Needless to say, it has the potential to revolutionise the investment decision process, and by relation, the world of growth and innovation.

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