While the application of Artificial Intelligence in Finance has a long tradition, its potential in Entrepreneurship has been intensively explored only recently. In this context, Entrepreneurial Finance is a particularly fertile ground for future Artificial Intelligence proliferation. To support the latter, the study provides a bibliometric review of Artificial Intelligence applications in (1) entrepreneurial finance literature, and (2) corporate finance literature with implications for Entrepreneurship. Rigorous search and screening procedures of the scientific database Web of Science Core Collection resulted in the identification of 1890 relevant journal articles subjected to analysis. The bibliometric analysis gives a rich insight into the knowledge field's conceptual, intellectual, and social structure, indicating nascent and underdeveloped research directions. As far as we were able to identify, this is the first study to map and bibliometrically analyze the academic field concerning the relationship between Artificial Intelligence, Entrepreneurship, and Finance, and the first review that deals with Artificial Intelligence methods in Entrepreneurship. According to the results, Artificial Neural Network, Deep Neural Network and Support Vector Machine are highly represented in almost all identified topic niches. At the same time, applying Topic Modeling, Fuzzy Neural Network and Growing Hierarchical Self-organizing Map is quite rare. As an element of the research, and before final remarks, the article deals as well with a discussion of certain gaps in the relationship between Computer Science and Economics. These gaps do represent problems in the application of Artificial Intelligence in Economic Science. As a way to at least in part remedy this situation, the foundational paradigm and the bespoke demonstration of the Monte Carlo randomized algorithm are presented.

翻译：尽管人工智能在金融领域的应用已有悠久传统，但它在创业领域中的潜力直到最近才得到深入探索。在此背景下，创业金融成为未来人工智能扩展尤为肥沃的土壤。为支持这一进程，本研究对人工智能在（1）创业金融文献及（2）与企业创业相关公司金融文献中的应用进行了文献计量综述。通过对科学数据库Web of Science核心合集进行严格的检索与筛选程序，最终识别出1890篇相关期刊文章并纳入分析。文献计量分析揭示了该知识领域的概念结构、知识结构与社会结构，指出了新兴且尚不成熟的研究方向。据我们所知，这是首项对人工智能、创业与金融三者关系进行学术领域图谱绘制与文献计量分析的研究，也是首篇涉及人工智能方法在创业领域应用的综述。结果表明，人工神经网络、深度神经网络与支持向量机在几乎所有识别出的主题领域中均高度出现；与此同时，主题建模、模糊神经网络与增长型层次自组织映射的应用则相对罕见。作为研究的组成部分，在最终结论之前，本文还讨论了计算机科学与经济学之间关系中的某些空白。这些空白确实构成了人工智能在经济科学中应用的问题所在。为至少部分缓解这一状况，本文提出了基础范式并展示了蒙特卡洛随机算法的定制化演示。