Predicting Business Angel Early-Stage Decision Making Using AI

• URL: http://arxiv.org/abs/2507.03721v1
• Date: Fri, 04 Jul 2025 17:17:34 GMT
• Title: Predicting Business Angel Early-Stage Decision Making Using AI
• Abstract summary: Business angels offer a critical source of funding, but their decision-making is often subjective and resource-intensive.<n>One such tool, the Critical Factor Assessment (CFA), has been evaluated post-decision and found to be significantly more accurate than investors' own decisions.<n>This study builds on previous work to investigate whether the constraints inhibiting its adoption can be overcome using a trained AI model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: External funding is crucial for early-stage ventures, particularly technology startups that require significant R&D investment. Business angels offer a critical source of funding, but their decision-making is often subjective and resource-intensive for both investor and entrepreneur. Much research has investigated this investment process to find the critical factors angels consider. One such tool, the Critical Factor Assessment (CFA), deployed more than 20,000 times by the Canadian Innovation Centre, has been evaluated post-decision and found to be significantly more accurate than investors' own decisions. However, a single CFA analysis requires three trained individuals and several days, limiting its adoption. This study builds on previous work validating the CFA to investigate whether the constraints inhibiting its adoption can be overcome using a trained AI model. In this research, we prompted multiple large language models (LLMs) to assign the eight CFA factors to a dataset of 600 transcribed, unstructured startup pitches seeking business angel funding with known investment outcomes. We then trained and evaluated machine learning classification models using the LLM-generated CFA scores as input features. Our best-performing model demonstrated high predictive accuracy (85.0% for predicting BA deal/no-deal outcomes) and exhibited significant correlation (Spearman's r = 0.896, p-value < 0.001) with conventional human-graded evaluations. The integration of AI-based feature extraction with a structured and validated decision-making framework yielded a scalable, reliable, and less-biased model for evaluating startup pitches, removing the constraints that previously limited adoption.

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