Application of Hybrid Intelligent Optimization Algorithm in Internal Audit Informatization Construction

With the booming growth of the big data era and the rapid advancement of information technology (IT), internal audit work in enterprises is facing unprecedented opportunities and challenges. Internal audit, as an independent and objective guarantee and consulting activity, aims to increase enterprise value, improve operational efficiency, and plays an irreplaceable role in the stable growth of enterprises. However, traditional internal audit methods are no longer able to meet the complex and ever-changing needs of modern enterprises. Therefore, strengthening IT construction and improving the intelligence level of internal audit have become an inevitable trend in industry growth. Machine learning (ML), as one of the core technologies of artificial intelligence (AI), has shown enormous potential in multiple fields due to its powerful data processing and pattern recognition capabilities. This article proposes an internal audit system based on hybrid intelligent optimization algorithms, which fully utilizes the advantages of ML technology and combines multiple intelligent optimization algorithms to build an efficient and intelligent internal audit solution. The experimental results show that the system proposed in this paper can significantly improve the intelligence level of audit work, reduce audit costs, improve audit quality, and provide strong technical support for the modernization of internal audit in enterprises.