Many would contend that the United States healthcare system should be moving towards a state of health equity. Here, every individual is not disadvantaged from achieving their true health potential. However, a variety of barriers currently exist that restrict individuals across the country from attaining equitable health outcomes; one of these is the social determinants of health (SDOH). The SDOH are non-medical factors that influence the health outcomes of an individual such as air pollution, food insecurity, and transportation accessibility. Each of these factors can influence the critical illnesses and health outcomes of individuals and, in turn, diminish the level of health equity in affected areas. Further, the SDOH have a strong correlation with lower levels of health outcomes such as life expectancy, physical health, and mental health. Despite having influenced the United States health care system for decades, the industry has only begun to address its influences within the past few years. Through exploration between the associations of the SDOH and health outcomes, programming and policy-making can begin to address the barrier to health equity that the SDOH create.

The growth of fintech companies in developing countries has led to increased levels of economic development and financial inclusion. This thesis explores the reasons for the success of these companies, with a focus on the impact they have on the local economy and their ability to provide financial services to underserved populations. The intent of this thesis is to educate the reader on the overall landscape of financial technology companies in developing countries. The writing will examine the specific types of services offered by these fintech companies that operate in developing countries and the catalysts that make them successful. It will also cover the impact that these companies have on the nations they operate in by looking at contributions to overall economic development and financial inclusion. The results of this literature will have implications for business leaders, policymakers, and investors interested in promoting financial inclusion and economic development through fintech.

2018, Google researchers published the BERT (Bidirectional Encoder Representations from Transformers) model, which has since served as a starting point for hundreds of NLP (Natural Language Processing) related experiments and other derivative models. BERT was trained on masked-language modelling (sentence prediction) but its capabilities extend to more common NLP tasks, such as language inference and text classification. Naralytics is a company that seeks to use natural language in order to be able to categorize users who create text into multiple categories – which is a modified version of classification. However, the text that Naralytics seeks to pull from exceed the maximum token length of 512 tokens that BERT supports – so this report discusses the research towards multiple BERT derivatives that seek to address this problem – and then implements a solution that addresses the multiple concerns that are attached to this kind of model.