Bayesian Network Modelling in the Audit of Listed Companies: Optimising Evaluation Strategies and Mitigating Risks through Addressing Information Asymmetry

In the audit of listed companies, the reason why information asymmetry creates significant challenges for auditors, is because that it hinders their ability to fully grasp the true operational status of enterprises, which in turn may escalate audit risks. As a probabilistic tool for modelling uncertainty, Bayesian networks could offer the ability for the integration of multiple sources of information while dynamically updating risk assessments, presenting a novel approach to addressing the problem of information asymmetry in audits. This paper begins with an analysis of the interrelationships among audit variables, develops an audit evaluation model, which could be established on Bayesian networks, and explores the application mechanisms in optimising evaluation strategies, quantifying risk probabilities, and mitigating the impacts of information asymmetry. Furthermore, the paper highlights the advantages, which could be based on this model, in managing uncertainty, adapting dynamically, and integrating multidimensional risk factors, for the purpose of providing a theoretical foundation for enhancing the audit quality of listed companies.