Optimization of RPA Audit Robot Design Based on Machine Learning

With the increasing complexity of audit requirements, traditional audit methods have exposed significant shortcomings in dealing with rapidly changing business environments and massive complex data. This article proposes a design of RPA audit robot based on machine learning optimization, which deeply integrates machine learning technology into traditional RPA architecture to solve the limitations of existing RPA. This article significantly improves the efficiency of audit task execution by integrating the HCIA architecture. The experimental results show that the proposed scheme performs excellently in improving task execution accuracy, anomaly detection capability, and system adaptability, especially in complex audit scenarios with broad application prospects.