Unveiling opportunities, predicting performance, and more, but data dispersion holds challenges!
Private markets, spanning from VC, over Buyouts, and Real Estate to Infrastructure and Private debt, are witnessing a transformation driven by artificial intelligence (AI).
Traditionally overwhelmingly analogue and heavily reliant on human intuition and experience, private markets investing is embracing AI to unlock hidden potential and make data-driven decisions.
This newfound focus goes beyond individual companies, the benefits of which are well covered, and delves into the world of private market funds, the vehicles that pool capital for these investments.
The potential benefits are many – all hold the promise to save time and money while increasing conversion rates and quite possibly risk adjusted returns.
Benefits of artificial intelligence for capital allocation
Unveiling hidden gems
Smarter and faster due diligence
Predicting future performance
Tailored fund recommendations
Benefits of artificial intelligence for fundraising
Targeted investor identification
Personalized investor communication
Predictive fundraising outcomes
Enhanced investor due diligence support
Here's how AI is reshaping the way LPs and GPs approach private marketing funds.
Artificial intelligence has the potential to drive several benefits for LPs
Unveiling hidden gems
AI algorithms can analyse vast datasets, encompassing financial records, news archives, and even social media sentiment. This allows them to identify promising private market funds that might otherwise fly under the radar of traditional methods. By uncovering subtle patterns and connections, AI can reveal hidden high potential gems– a game-changer for investors seeking idiosyncratic investment opportunities and not just an automatic re-up.
Smarter and faster due diligence
The due diligence process for private market funds, even for a re-up, is arduous and time-consuming. AI can streamline this process by analysing mountains of legal documents, historical performance data, and industry reports. AI can highlight potential risks, identify inconsistencies, and summarise key information, saving valuable time for human analysts. This allows investors to focus on in-depth analysis of the most promising funds.
Predicting future performance
AI can be trained on historical data of private market funds and market trends. This allows it to generate predictive models that forecast the potential performance of a fund. While not a crystal ball, these insights can significantly aid investors in making informed decisions. Imagine comparing different funds and having an AI-powered assessment of their projected returns and risk profiles.
Tailored fund recommendations
AI can analyse an investor's risk tolerance and specific investment goals. Based on this, it can recommend suitable private market funds that align with their specific needs. This personalized approach ensures investors are not just chasing high returns or simply re-active to the opportunities that present themselves but are strategically building a diversified portfolio that matches their risk appetite.
For LPs AI has the potential to be a game changer across their processes and save them significant time and money.
Investment benefits for GPs
This has been a hot topic for a while, and many GPs are already in advanced stages of implementing AI in their investment processes. AI can be used to identify high-potential investments, improve deal flow management, data-driven decision making and much more.
Much less discussed is how AI can help enhance fundraising for GPs
Rather than work off stale erroneous databases AI can help with targeted investor identification
AI can analyse vast datasets on investor behaviour, preferences, and investment history. This allows GPs to identify high-potential LPs who are a good fit for their fund's investment strategy and risk profile. Imagine having AI pinpoint LPs who consistently invest in similar funds to yours, or where your fund fits in their portfolio construction.
Personalised investor communication
With insights from AI, fund managers can tailor their communication to resonate with specific LPs and LP segments. AI can help generate customised pitch decks and marketing materials that highlight aspects of the fund most likely to attract each LP.
Predictive fundraising outcomes
AI models can be trained on historical data to predict the likelihood of success for a fundraising campaign. By analysing factors like market conditions, LP sentiment, and the fund's past performance, AI can provide valuable insights to guide fundraising strategies and resource allocation. Imagine having an AI-powered assessment of your fundraising goals and potential roadblocks before you even begin.
Enhanced investor due diligence
The due diligence process is crucial for both investors and fund managers. AI can analyse legal documents, financial statements, and other data points to identify potential issues or inconsistencies. This streamlines the process for investors, allowing fund managers to focus on building trust and addressing any concerns effectively.
Using an AI data-driven approach can significantly improve the effectiveness of fundraising efforts and help save time and resources for GPs. Higher conversion rates and faster fund raises.
But a data chasm hinders the promise of AI
Unlike the public markets with their centralized repositories like Bloomberg, private market data is scattered across numerous sources, creating a significant data chasm. Imagine a vast ocean of valuable insights, but with no easy way to navigate or collect it. This data is held captive in silos, with key pieces residing with various market participants:
GPs possess a wealth of information but only on their own funds and investments, which they rightly regard as confidential and wont readily share
LPs, especially the ones with large portfolios by count and long histories hold valuable data mainly on the funds in which they have invested but this information is typically fragmented, unstructured, and not accessible to everyone
Data Vendors, while they aggregate and sell private market data, their offerings are often incomplete and lack the depth needed for robust AI models
The consequence? No single entity possesses a comprehensive dataset sufficient for truly powerful AI applications
This fragmented landscape makes it challenging to train AI models effectively for private markets. Imagine trying to build a house without a complete set of blueprints – the structure might be shaky, and the functionality limited.
This data dispersion creates a two-fold challenge;
Finding an efficient way to collect the data from disparate sources
Ensuring the quality of the data
Without doing so, the AI model will basically be built and trained on conflicting or unreliable data, leading to poor models -with the result that the insights generated at best are misleading and at worst downright wrong.
Only by addressing the data dispersion challenge, can private markets unlock power of AI.
This would revolutionise the way LPs and GPs approach private markets, leading to a more efficient and lucrative landscape for all participants.
Stay illiquid!
Kasper
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Sources: Balentic 2024, PE-Compass 2024