Task Allocation of Financial Robots Based on RPA and Big Data Algorithms

With the continuous progress of technology, more and more enterprises are adopting automation technology to replace traditional financial work models. The use of financial robots can improve the accuracy and efficiency of task allocation, and improving the overall work efficiency of enterprises is still an urgent problem to be solved. This article combined Robotic Process Automation (RPA) and big data algorithms to optimize the fuzzy C-means clustering algorithm model by adjusting the membership function. The model was integrated into the RPA framework based on the characteristics of financial tasks and combined with big data algorithms to achieve task allocation. The experimental results showed that the highest accuracy of task allocation for financial robots based on RPA and big data algorithms was 98.63%. When assigning tasks of the same type, the accuracy was better than that of financial robots based on manually formulated rules, up to 14.51 %. This indicates that task allocation for financial robots based on RPA and big data algorithms can be applied in practice.