Financial Robot Task Allocation Based on RPA and Big Data Algorithms

Abstract

Since the current enterprise financial task allocation is difficult to adapt to the complex and changing business environment, it leads to low allocation efficiency, uneven resource utilization and insufficient exception handling capabilities. This paper implements an intelligent task allocation method based on the combination of RPA (Robotic Process Automation) technology and big data algorithm. This method uses RPA technology to analyze task characteristics, adopts random forest algorithm to classify tasks, and uses support vector machine to predict task priorities. Then, the genetic algorithm is combined to optimize the task scheduling strategy to achieve dynamic task allocation. At the same time, the isolation forest algorithm is used to monitor the task execution status in real time and dynamically adjust the task allocation order. And the classification model, scheduling strategy and allocation rule base are continuously optimized through the feedback mechanism. In the four-week financial robot task allocation comparison experiment, the resource utilization rate of this method was improved from 85.4% to 90.4%, and the exception response time was kept between 4.5 and 6.4 minutes. The experimental results verify the effectiveness of this method in improving the efficiency of financial task allocation, optimizing resource utilization and enhancing the exception response capability, which has important academic value and application prospects.