Web Matching System for Entrepreneurs and Investors

Abstract

Small and medium enterprises (SMEs) and startups are seen as a key source of economic growth, production and innovation. Nevertheless, the traditional funding sources’ supply, such as bank lending, venture capitals and angel investors, couldn’t fulfil the needed demand, resulting in the financing gap. Over time, several web platforms have come to the fore for the purpose of connecting entrepreneurs and investors together by using different systems, such as faceted search systems (FSSs) and automated investment systems (AISs). The FSSs enable investors to search for the entrepreneurs’ investment rounds by applying multiple facets and filters. The AISs automatically diversify the investors’ investments on multiple companies’ investment rounds created by the entrepreneurs. However, both of these systems have a high failure rate due to the category-driven results of the FSSs in addition to the limited offerings and the high-risk factors of the AISs. For these reasons, the proposed web platform tackles these issues by a newly developed artificial intelligence matching system using machine learning as well as natural language processing as its foundation. In which, the proposed system matches investors, entrepreneurs and partner-seekers together. The system provides intelligent data-driven matches one at a time, while continuously updating the data each time a user refuses or approves a match, leading to more attainable results. In this report, the planning, analysis and design stages of the proposed platform are introduced using the Waterfall software development life cycle (SDLC) model.