Presentation Delivered at the Northwestern Mutual Data Science Institute Symposium 'Untapped AI - Next Frontiers'

In this research project ("Using Artificial Intelligence and Machine Learning to Evaluate the Efficacy of the JOBS Act"), our goal is to create a dynamic database consisting of structured and unstructured data sets using artificial intelligence and machine learning techniques to gauge the effectiveness of the Jumpstart Our Business Startups (JOBS) Act. Evaluating the success of the Act is challenging because of the complexity and extensive size of the data sets linked to key securities exemptions, such as Regulation Crowdfunding and Regulation A. In this study we will conduct a comprehensive literature review and collect securities registration data from the SEC's Electronic Data Gathering, Analysis, and Retrieval system. Following data cleaning and pre-processing procedures, we will use various algorithms to analyze the data with techniques such as sentiment analysis. The expected results will yield more accurate and efficient evaluations, reveal patterns and trends, and offer valuable insights to policymakers, regulators, issuers, and investors. The findings may be used to address key funding challenges faced by Milwaukee-area and Southeastern Wisconsin small businesses and entrepreneurs