Strategic research on improving accounting audit quality and risk control using big data

One of the urgent challenges in auditing today is preventing accounting management risk. This study integrates big data auditing technology to enhance audit quality by developing an audit risk assessment index system based on material misstatement risk and inspection risk. By combining the hierarchical analysis and entropy weighting methods to assign risk indicators, the accounting audit risk index for Company Z was calculated using a multi-level fuzzy comprehensive evaluation method and regression analysis to examine impact factors. Empirical evidence shows that the overall expected audit risk is 0.412—indicating a low to average risk level—with significant correlations between the previous year’s audit opinion, audit fee, and other factors such as the largest shareholder’s holding, board size, percentage of independent directors, operating income growth, net profit, and the audit environment. The study focuses on developing effective prevention and response strategies in the era of big data and offers recommendations to reduce potential auditing risks.