This study examines the strategic digital transformation of financial shared services (FSS) in higher education using big data analytics. By leveraging a multifaceted dataset from diverse institutions, including financial transaction records, operational metrics, user feedback, and system logs, we employ an extensive research methodology. This encompasses data preprocessing, descriptive statistics, exploratory data analysis, predictive modeling, cluster analysis, sentiment analysis, and time series analysis. Our findings indicate substantial improvements in operational efficiency, evidenced by reduced processing times and error rates, as well as significant cost savings. User satisfaction scores and sentiment analysis reveal a positive trend following the transformation. Predictive models exhibit high accuracy in forecasting key outcomes, highlighting the transformative impact of digital strategies on higher education FSS. This research provides valuable insights for institutions aiming to enhance their financial operations through digital innovation.
Jiang et al. (Thu,) studied this question.