Takas, N., Kouloumpris, E., Moutsianas, K., Liapis, G., Vlahavas, I., & Kousenidis, D. (2024). Startup Sustainability Forecasting with Artificial Intelligence. Applied Sciences, 14(19), 8925. https://doi.org/10.3390/app14198925
In recent years, we have witnessed a massive increase in the number of startups, which are also producing significant amounts of digital data. This poses a new challenge for expert analysts due to their limited attention spans and knowledge, also considering the low success rate of empirical startup evaluation. However, this new era also presents a great opportunity for the application of artificial intelligence (AI) towards intelligent startup investments. There are only a few works that have considered the potential of AI for startup recommendation, and they have not paid attention to the actual requirements of investors, also neglecting to investigate the desirability, feasibility, and value proposition of this venture. In this paper, we answer these questions by conducting a survey in collaboration with three major organizations of the Greek startup ecosystem. Furthermore, this paper also presents the design specifications for an AI-based decision support system for forecasting startup sustainability that is aligned with the requirements of expert analysts. Preliminary experiments with 44 Greek startups demonstrate Random Forest’s strong ability to predict sustainability scores.
