Design and Smart Audit System Using PSO Algorithm

Internal audit is an important function of enterprises, and its effective operation is directly related to the healthy development of enterprises. In the network environment, the process of enterprise informatisation is accelerating, and the internal audit of enterprises can face more and more complex multi-source data. In such an environment, the traditional audit mode can no longer meet the needs of the new situation. Using a variety of deep learning methods such as web crawler, visual analysis, recurrent neural network natural language processing, convolutional neural network image processing, etc., it can establish an intelligent auditing system, break the information barrier and data barrier of management, assets, business and other aspects, quickly and accurately locate and describe the problems found by auditing, do the most basic work in the auditing business, improve the efficiency of business processing, and reduce the number of Auditor's mechanical man-hours, which is an inevitable trend. In the field of auditing, traditional methods face challenges in handling big data and complex calculations. This study designed an intelligent audit system using the Particle Swarm Optimization (PSO) algorithm, which can automate data analysis and risk assessment. By combining audit knowledge and machine learning techniques, it not only improve the efficiency of auditing, but also enhance the accuracy of decision-making. Research has shown that PSO optimization methods are more accurate in identifying abnormal projects. This new audit system has important practical and theoretical value in improving audit quality and efficiency.