Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm

Abstract The study conducts a bibliometric review of artificial intelligence applications in two areas: the entrepreneurial finance literature, and the corporate finance literature with implications for entrepreneurship. A rigorous search and screening of the web of science core collection identified 1,890 journal articles for analysis. The bibliometrics provide a detailed view of the knowledge field, indicating underdeveloped research directions. An important contribution comes from insights through artificial intelligence methods in entrepreneurship. The results demonstrate a high representation of artificial neural networks, deep neural networks, and support vector machines across almost all identified topic niches. In contrast, applications of topic modeling, fuzzy neural networks, and growing hierarchical self-organizing maps are rare. Additionally, we take a broader view by addressing the problem of applying artificial intelligence in economic science. Specifically, we present the foundational paradigm and a bespoke demonstration of the Monte Carlo randomized algorithm.