Bridging the innovation gap: An AI and game-theoretic framework for optimizing angel investor-startup matching

Background and Motivation

The process of connecting promising startups with the right angel investors has long been more of an art than a science. Relying on fragmented networks, gut feelings, and subjective criteria often leads to missed opportunities for both parties. Startups struggle to find investors who truly align with their vision and needs, while investors spend significant resources sifting through opportunities to find the perfect fit. This research was motivated by the critical need to bring clarity, efficiency, and strategic insight to this vital, yet opaque, stage of funding. The paper aims to replace intuition with intelligence, creating a systematic method for understanding what drives successful investment matches.

Methodology and Scope

This research utilises a novel dual-methodology approach to dissect the investment matching process. First, the study leverages the powerful natural language processing capabilities of AI engines, specifically Gemini and ChatGPT 4, to analyse historical investment patterns and behaviours. These AI systems mine real-world data to identify the most influential and nuanced criteria that angel investors prioritise when evaluating startups.

Second, the research employs R programming to create sophisticated simulations. These simulations test the predictive quality of the AI-identified criteria and utilise statistical optimisation techniques, specifically the Youden index, to determine optimal cutoff values. This balances sensitivity (finding all potential good matches) and specificity (avoiding bad matches), effectively creating a refined and efficient matching filter grounded in game-theoretic principles.

Key Findings and Contributions

The study's findings reveal that the most effective matching model is not reliant on a single type of data. Instead, it requires a hybrid framework that integrates both qualitative preferences (e.g., founder passion, market vision, strategic alignment) and quantitative criteria (e.g., financial projections, market size, traction metrics).

By comparing results from different AI models and methodological approaches, the research provides unprecedented insights into the dynamic complexities of the investment landscape. It is the first known study to apply large-language model AI technologies specifically to the angel investor-startup matching process, marking a significant academic and practical contribution to the field of early-stage investing.

Why It Matters

• Increased deal flow efficiency for investors, saving time and capital.
• Higher likelihood of funding for qualified startups that match an investor's true criteria.
• Stronger, more strategic post-investment relationships built on aligned expectations and goals.
• A healthier, more dynamic, and data-informed entrepreneurial ecosystem.

Practical Applications

• Angel Investors & Venture Capitalists: Can use this framework to systematise their deal-sourcing and due diligence processes, building a more robust and data-backed investment thesis.
• Startups & Entrepreneurs: Can gain a clearer understanding of investor priorities, allowing them to better target their pitches and business plans to align with investors.
• Platforms & Intermediaries: Accelerators, incubators, and online investment platforms can integrate these insights to significantly improve their matching algorithms, providing greater value to their users.
• Academic Researchers: Provides a new methodology at the intersection of AI, game theory, and finance, opening doors for further interdisciplinary study.

Discover high-quality academic insights in finance from this article published in China Finance Review International. Click the DOI below to read the full-text original!