Crowdfunding Success: Human Insights vs Algorithmic Textual Extraction

Using a unique dataset of equity offerings from crowdfunding platforms, we explore the synergy between human insights and algorithmic analysis in evaluating campaign success through business plan assessments. Human evaluators (students) used a predefined grid to assess each proposal in a Business Plan competition. We then developed a classifier with advanced textual representations and compared prediction errors between human evaluators, a machine learning model, and their combination. Our goal is to identify the drivers of discrepancies in their evaluations. While AI models outperform humans in overall accuracy, human evaluations offer valuable insights, especially in areas requiring subtle judgment. Combining human and AI predictions leads to improved performance, highlighting the complementary strengths of human intuition and AI's computational power.