Evaluating the Impact of Digitalization on Financial Inclusion in Nigeria: A Machine Learning Approach

This work investigates the ways in which digital technology is helping people in Nigeria to get access to financial service, by applying machine learning to examine this relationship. Helping the poor and growing the economy with financial inclusion still proves difficult in Nigeria, mainly affecting people living in rural areas, white females and those who work informally. The fast uptake of digital financial services can help fill this gap by making finance more affordable and accessible for those who usually do not use banks. Even so, there is little research into how machine learning helps understand this impact. By using machine learning tools, the research watches for trends in financial behavior, tries to identify why some people adopt certain services and seeks out infrastructure, knowledge and rules issues. The research also analyses theories called TAM and DOI that help explain why DFS is now used more widely. According to the findings, digitalization can greatly increase inclusion in financial systems as long as ethical issues in machine learning are considered. Suggestions are; making rural services better, making financial literacy more common and encouraging everyone involved to cooperate for inclusive growth. The study adds value by bringing together machine learning insights and measures for public policy, helping to learn more about how digitalization can help Nigeria achieve financial inclusion in a sustainable way.