In view of the lack of accuracy and flexibility faced by traditional robotic process automation financial automation process systems when processing complex and changeable financial data, this study designs a financial automation process system based on data mining. The system uses data mining technology to analyze a large amount of historical financial data, thereby intelligently identifying and optimizing the key steps in the financial process. The results found that the system can achieve a minimum automation rate of 97.36%. In the financial process, the average efficiency of the system has also reached about 85%, and the execution accuracy is above 90%. It is proved that this system can not only significantly improve the automation and accuracy of financial processes, but also effectively respond to anomalies and changes in financial data, improving overall financial operation efficiency.