Design and Implementation of Internal Audit Information Platform System in Colleges and Universities

Abstract

This study developed an internal audit information platform for universities to address the challenges of data integration, process control, and risk identification. The platform was created to enhance audit efficiency, transparency, and decision-making in higher education governance, responding to issues like fragmented data, manual processes, and delayed risk detection. It integrates multi-source data via extract-transform-load processes, employs a rule engine with logical expressions, and uses behavioral modeling and a time series analysis for risk identification, supported by a visualization module. The results revealed a 98.9% data integration accuracy, a 93.5% risk detection hit rate, and stable performance under high concurrency, though complex rules increased response times. The platform improved audit coverage, response speed, and resource allocation, offering a scalable, intelligent solution for university governance, with potential for broader application despite challenges in resource consumption and model interpretability.