Integrating Robotic Process Automation and Machine Learning for Enhanced Document Intelligence

Abstract

This research addresses the integration of Robotic Process Automation (RPA) and Machine Learning (ML) to enhance the efficiency and accuracy of document processing in various business environments. The study identifies the challenges associated with traditional document processing methods, which are often time consuming and error-prone, and explores how the synergistic application of RPA and ML can mitigate these issues, thereby revolutionizing document handling practices. The methodology adopted involves a combination of RPA to automate routine document handling tasks and ML to intelligently classify and extract data from a wide range of document formats. This integration was prototyped using an advanced ML model trained on a labeled dataset to recognize and categorize document contents accurately to finance document. The RPA component was developed to seamlessly fetch, process, and route documents based on the insights derived from the ML model, ensuring a streamlined workflow. Initial evaluation of the prototype demonstrated an 95% classification accuracy for financial documents, supported by an AUC-ROC score of 0.91. The confusion matrix analysis revealed a 3% false-positive rate. Additionally, the system achieved a real time processing capability of up to 80 pages of document per minute, highlighting the robustness and scalability of the integrated RPA and ML solution.