Big Data‑Enabled Intelligent Pathways for University Financial Auditing: Evidence from Guangdong "Yue‑Style Applications"

Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v6i6.4663

Peitian Su, Xiongwei Wu

Guangzhou Institute of Science and Technology, Guangzhou, Guangdong, China

Abstract

Big data is reshaping university financial management and internal auditing. Using Guangdong Province and its distinctive "Yue‑style applications" as a case, this paper examines how data‑driven technologies transform the foundations, processes and outcomes of university financial auditing. It first reviews the current use of big data in university financial management and identifies the limitations of traditional, sample‑based audit approaches. It then explains why Guangdong provides a representative case, highlighting its strong higher‑education system, digital campus construction and supportive policy environment. On this basis, the paper proposes an implementation pathway for intelligent auditing that integrates data warehouses, indicator systems, analytic models, process automation and visual dashboards. Typical practices in Guangdong universities, including smart audit platforms, risk‑warning models and RPA‑based "audit robots", are used to illustrate improvements in efficiency, coverage and risk prevention. Finally, the paper discusses future trends toward cross‑domain data ecosystems and more explainable algorithms, arguing that big‑data‑enabled auditing offers a viable route to modernising financial governance in Chinese universities.

Keywords

big data, university financial auditing, intelligent pathway, Yue‑style applications.

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