Construction of a social audit platform based on big data for “industry price, quality and credit”

Abstract

Abstract As the latest product of the innovative integration of information technology, big data has had a huge impact on many fields. Big data technology has changed the traditional audit method, expanded the scope and field of auditing, and injected vitality into the national big data audit work. Full audit coverage has given social auditing a new mission and posed new challenges to creating mega data audit platforms. Driven by the overall goal of full audit coverage, the construction and innovation of a big data audit platform are particularly important. This subject combines data-related technologies and uses more mature data mining algorithms to try to establish an audit platform, and analyzes the three aspects of requirements, concepts and strategies for building an audit platform, dividing it into five modules: data center, acquisition, pre-processing, analysis and data visualization. The research results show that by predicting the revenue indicators of Company A, the overall error of the prediction results of the big data audit platform is controlled at 0.79%, which is better than 8.42% of the traditional audit model; by scoring the two audit models, the score of the big data audit method is 0.7024 higher than that of the traditional model, which is 0.3933, indicating that its accuracy, efficiency comprehensiveness, sustainability, and low risk are In this way, this paper explores a new effective audit path and method, which provides a good basis for the enterprise’s “industry price, quality and trust payment” and guides the way forward for future mega Data audits.