Matching angels to startups using AI

Purpose This paper introduces an innovative approach to analyzing the matching process between angel investors and start-up companies, utilizing a combination of artificial intelligence (AI) technologies and game-theoretic models. By harnessing the capabilities of AI engines such as Gemini and ChatGPT 4, we extract valuable insights from real-world data, identifying influential criteria that drive investment decisions on both sides of the equation. Design/methodology/approach Leveraging historical investment patterns and behaviors, Gemini identifies key criteria that angel investors prioritize when evaluating start-up opportunities. We then employ R programming simulation to evaluate the predictive quality of these criteria and optimize cutoff values using the Youden index, balancing sensitivity and specificity in the matching process. Findings Our findings underscore the importance of integrating qualitative preferences and quantitative criteria in the matching process, offering a comprehensive framework for guiding strategic investment decisions in the entrepreneurial ecosystem. By comparing and contrasting the results obtained from different methodologies, we gain valuable insights into the complexities and dynamics of the investment landscape, paving the way for informed decision-making and strategic partnerships. Our research contributes to advancing the field of angel investing, offering practical insights and tools to support investors, entrepreneurs and stakeholders in navigating the dynamic and evolving landscape of early-stage investment. Originality/value This is the first attempt to utilize AI in the matching process between startups and angel investors.