In the fast-paced and intricate world of investment firms, the decision-making role of the investment committee is essential, particularly when it involves assessing potential investments in target organizations. Central to this process is the investment memo, a detailed and rigorously prepared document by investment advisors.
Traditionally, the preparation of these memos is a demanding task, requiring extensive due diligence that includes a thorough examination of target company documents in data rooms, extraction of insights from internal and external sources, and comprehensive market research. Investment analysts play a crucial role in this labour-intensive process, analysing potential investments and crafting memos that outline the strengths, weaknesses, and investment recommendations for the target company.
This paper examines the application of Large Language Models (LLM) in enhancing the efficiency and accuracy of this process. LLMs offer a novel approach to analyse and synthesise data from diverse sources such as data rooms, internal knowledge bases, and external databases. We explore how LLMs can assist in generating initial investment hypotheses, drafting preliminary versions of investment memos, and providing rapid and precise Question and Answer (Q&A) support.
Our initial findings demonstrate a notable increase in process efficiency, showing a 2x acceleration in tasks ranging from due diligence to the review of decisions by investment committees. Additionally, this method achieves an 85% accuracy rate in the responses obtained from these comprehensive documents, showcasing the potential of LLMs to revolutionise the manual and time-consuming aspects of investment decision-making in investment firms.
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