Generative AI in Private Equity

Artificial intelligence (AI), and more specifically generative AI, has been one of the most recent groundbreaking developments in technology. Generative AI systems are capable of generating text, images, or other media in response to written prompts. Generative models acquire knowledge about patterns and structure of the input data, enabling them to produce fresh content that shares similarities with the training data while incorporating a certain level of originality.

Large language models (LLM) like OpenAI's ChatGPT and Google's Bard have taken the world by storm. They've altered the way many businesses operate, and though the technology is still new and has room to develop, many are already finding new and innovative ways to use these tools. Industries have begun to feel the impacts of AI and private markets are no exception. AI has already proven to be a helpful tool for private market professionals in spotting new opportunities, reducing back-office activities, and accelerating dealmaking.

Identifying Opportunities

By analysing large amounts of data, generative AI is able to help identify potential investment opportunities. The use of natural language processing (NLP) algorithms to analyse vast amounts of text data, such as news stories and social media posts, to find possible investment opportunities is one example of how generative AI has been utilised for investment analysis. Historically, LLMs were unable to deal with large context windows, but this is changing fast, particularly with models as big as GPT4 from OpenAI.

Reducing back-office activities and accelerating dealmaking

Private equity (PE) companies have been using AI more and more to automate and streamline numerous deal-related operations. Due diligence, financial modelling, and data analysis are a few examples of these jobs that can be time- and resource-intensive. By leveraging AI-powered tools and technologies, PE firms can speed up the deal process, reduce costs, and make more informed investment decisions.

AI can also be used to automate repetitive and tedious tasks like contract review, document processing, and compliance checks. This can free up resources to focus on higher-value tasks like strategy development and relationship building. The newer LLMs are even able to write highly accurate investment memo summaries, or generate textual analysis about companies that PE firms are looking to acquire.

Use with care

However, there are still technical issues that it's important to be aware of. Hallucinations are still common, meaning that while answers are convincing, they can be made up. This is more likely the larger the context window provided. Careful constriction of prompts and prompt engineering can reduce these effects.

Another, arguably more serious, issue is that the widespread use of AI can exacerbate current systemic biases, which the industry has previously highlighted as a concern even before factoring machines. The degree of impact primarily depends on the quality of data that is used to train the AI systems.

The effectiveness of AI depends on the quality of data it is fed, and as it advances in its reach and capabilities, there may be an increasing number of factors for engineers and users to take into account.

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Header Image by Alexandre Debiève