Development of a data-driven engine for the private investment sector

点击翻译按钮获取中文摘要

The enthusiasm for entrepreneurship has significantly increased in recent years in the high technology sector. As a result, the many innovative projects that are in the early stages of their life cycle sometimes need refinement both in terms of the value proposition they suggest and the structure of the company that supports them. It is in this perspective that private investment firms and accelerators like Holt play a critical role. They support start-up companies in defining their business model, tracing the technological roadmap and designing a marketing plan. However, given the premature nature of their intervention and the high level of risk of the investment, better control over the entire investment decision process is required: from the discovery of startups with high potential to the allocation of funds to these companies. Briefly, what the project aims to achieve is the development of a solution for venture capital firms, VC as a Service (or VCaaS), that would significantly improve the investment decision process of its users through a data-driven approach. This involves, among other things, establishing a portrait of companies by identifying all the factors that could have an impact on the quality of the investment. One of the important factors in this process is the quality and objectivity with which a startup's potential is assessed within its technological and economic context. This requires specialized experience and skills in the field of expertise of the company. Indeed, this assessment is carried out by investment experts (referred to as advisors), selected from a pool of collaborators with diverse profiles. The right match between a startup and advisor is therefore crucial for the success of an investment portfolio. Optimizing this process through data science requires the use of advanced artificial intelligence techniques in the fields of information retrieval systems, recommendation systems, natural language processing, supervised learning, as well as reinforcement learning.