Artificial intelligence in early-stage venture capital: an empirical study of screening and evaluation processes
Lombardi, Ferdinando (A.A. 2024/2025) Artificial intelligence in early-stage venture capital: an empirical study of screening and evaluation processes. Tesi di Laurea in Advanced corporate finance, Luiss Guido Carli, relatore Raffaele Oriani, pp. 97. [Master's Degree Thesis]
![[img]](http://tesi.luiss.it/style/images/fileicons/application_pdf.png) |
PDF (Full text)
Restricted to Registered users only Download (1MB) | Request a copy |
Abstract/Index
The venture capital ecosystem: structures, processes and evolution. The nature and economic function of venture capital. Venture capital within the spectrum of corporate financing. Types of venture capital funds and stage-based investment. The structure and life cycle of a venture capital fund. The venture capital investment process. Institutional and market evolution of venture capital. Artificial intelligence in finance: technologies, prompt engineering and risks. Artificial intelligence: conceptual origins and theoretical foundations. Core technologies of artificial intelligence. Prompt engineering and the controllability of large language models. Limits, risks and biases of artificial intelligence: transparency, explainability, and herd behavior. Regulatory and governance frameworks for artificial intelligence: the AI act, the Italian strategy and the European model. Applications of artificial intelligence in corporate finance. Artificial intelligence and venture capital. Methodology: designing the automated screening framework. AI and venture capital: state of the art and possible applications. AI support for venture capitalists. Automated screening and deal flow. AI support for preliminary due diligence: team, market and technology. Empirical analysis: evaluating AI-assisted preliminary due diligence. Experimental design and methodology. Construction of the dataset and evaluation criteria. Quantitative analysis of results. Qualitative analysis: phenomenology and interpretative models.
References
Bibliografia: pp. 89-92.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree program in Corporate Finance, English language (LM-77) |
| Chair: | Advanced corporate finance |
| Thesis Supervisor: | Oriani, Raffaele |
| Thesis Co-Supervisor: | Vulpiani, Marco |
| Academic Year: | 2024/2025 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 01 Jul 2026 16:07 |
| Last Modified: | 01 Jul 2026 16:07 |
| URI: | https://tesi.luiss.it/id/eprint/46289 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
