AI for Investment: A Platform Disruption
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Abstract (arXiv)
With the investment landscape becoming more competitive, efficiently scaling deal sourcing and improving deal insights have become a dominant strategy for funds. While funds are already spending significant efforts on these two tasks, they cannot be scaled with traditional approaches; hence, there is a surge in automating them. Many third party software providers have emerged recently to address this need with productivity solutions, but they fail due to a lack of personalization for the fund, privacy constraints, and natural limits of software use cases. Therefore, most major funds and many smaller funds have started developing their in-house AI platforms: a game changer for the industry. These platforms grow smarter by direct interactions with the fund and can be used to provide personalized use cases. Recent developments in large language models, e.g. ChatGPT, have provided an opportunity for other funds to also develop their own AI platforms. While not having an AI platform now is not a competitive disadvantage, it will be in two years. Funds require a practical plan and corresponding risk assessments for such AI platforms.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 6 Sep 2023.
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3 of 19 references.
References · 3
- 2016Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Modelscited by 10
- 2021Finding the unicorn: Predicting early stage startup success through a hybrid intelligence methodcited by 10
- 2023A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Modelscited by 3
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