The future of venture capital decision making

: the impact of quantitative sourcing and machine learning on the VC Investment process

Abstract

Investing in early-stage startups is a difficult endeavor. Venture Capitalists use heuristics and base their decisions on past experiences, which can lead to biases. Recently, Venture Capitalists are increasingly using artificial intelligence and quantitative sourcing to support their investment process, while others still rely on traditional investment mechanisms. This research investigates the usage and impact of artificial intelligence and machine learning throughout the venture investment cycle to make investment decisions. This dissertation is an exploratory study that employs a qualitative research approach in the form of semi-structured interviews with ten European Venture Capitalists. The results show that Venture Capitalists utilize machine learning and web scraper tools, particularly during the deal origination, firm-specific screening, and general screening stages of the investment process, to solve the identification and selection challenges. As a result, investment processes become more efficient and less biased, allowing for more time to be spent advising and mentoring portfolio startups. It adds to the existing literature on how artificial intelligence and data can augment existing investment mechanisms during the venture capital decision-making process.

Date of Award	18 Oct 2022
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	José Manuel Vasconcelos Silva e Sousa (Supervisor)