Research and Optimization of Audit Risk Assessment Model Based on Regression Algorithm

Recently, auditors have used audit risk models’ conceptual tools to assess and control the many risks that could occur during an audit. The tool guides the auditor in determining the necessary evidence for each relevant allegation and the required categories of evidence. The importance of refining audit risk assessment models depends on the crucial role that these models perform in allocating resources and reducing financial disparities. The challenging characteristic of such an audit risk assessment model is that inaccurate information can lead to incorrect risk assessments, legal frameworks and failure to detect material misstatements in financial statements. Hence, in this research, BP neural network-enabled machine learning (BPNN-ML) technologies have been improved for the audit risk assessment model. In that financial disparity, the regression algorithm was used to establish the audit risk assessment for data processing and entirely monitor it. The suggested method provides a flexible framework that may be utilized by a wide range of organizations, including global enterprises and financial institutions, to optimize audit processes and ensure regulatory compliance. This research adds to advancing auditing procedures and regulatory compliance efforts in contemporary company contexts by addressing the issues inherent in traditional methods and proposing a practical approach to these concerns. The experimental analysis of BPNN-ML outperforms physical monitoring in terms of feature importance ranking analysis, contextual adaptability analysis, sensitivity analysis, performance analysis and optimized risk assessment analysis.