Unravelling the Research Themes of Financial Literacy Using Topic Modelling and Text Mining: A Machine Learning Perspective

Financial literacy is essential in business, economics, and finance as it empowers individuals and organisations with the knowledge and skills needed for sound financial decision-making. It enables effective risk management, investment maximisation, and economic growth through responsible financial practices. Despite significant research growth in financial literacy over the past two decades, there exists a gap in comprehensive reviews of its primary themes. The study addresses this gap by utilising a blend of bibliometric analysis and a machine learning technique (structural topic modelling) on a dataset of 1303 articles from Elsevier’s Scopus database (2002-2022). It primarily focuses on financial literacy, highlighting key themes, publication trends, and notable journals. The outcomes of structural topic modelling unravel four distinct thematic clusters: i) Financial literacy and financial planning, ii) Financial literacy and financial education, iii) Financial literacy and technology, and iv) Financial literacy and financial behaviour. "Journal of Financial Counselling and Planning" is a prominent source, contributing 92 articles (7.1% of the total). Future studies on financial literacy should explore the evolving requirements of people and communities, encompassing an understanding of digital platforms and blockchain technologies, enabling them to make well-informed financial choices. These results provide valuable insights into the ever-changing landscape and emerging research trends in financial literacy. They hold significant relevance for diverse audiences, including scholars, policymakers, financial institutions, and professionals.