Intelligent Internal Audit Platform Architecture Based on Machine Learning

With the development of science and technology and the improvement of people’s living standards, construction projects are developing towards large buildings, green buildings and intelligent buildings. However, with the increasing tasks of audit supervision and the increasing work pressure, the traditional computer audit methods have been difficult to meet the requirements of full coverage of audit supervision, and using machine learning to conduct government audit provides a new idea to solve the above problems. However, information technology has not been fully introduced into the internal audit work, and the internal audit informatization construction is characterized by localization and fragmentation, which leads to the mismatch between internal audit and enterprise informatization, and can not serve the development of enterprises well. The enterprise audit department should make every effort to build a new generation of Internal Audit Center, and put forward the intelligent internal audit data analysis process and internal audit application framework model on this basis, so as to transform the internal audit function from the traditional supervision and control function to the convenient and efficient digital audit and the intelligent self audit function of robot process automation. Combined with the internal audit theory and practice and program design method, this paper analyzes and puts forward the intelligent internal audit platform architecture based on machine learning from the application level, so as to make the internal audit work move forward in the direction of high quality, high efficiency and high accuracy, and realize the breakthrough and innovation of artificial intelligence in the field of internal audit. The application of intelligent internal audit platform based on machine learning can not only effectively solve the problems of slow speed and error prone when traditional audit technology processes massive social security data, but also help the audit team determine the audit focus, explore audit doubts, and improve the utilization of audit data resources.