Automation in business processes has been accelerating over the last several years, especially with enterprises leveraging Robotic Process Automation (RPA) to automate repetitive, transactional activities performed by employees – thereby bringing in significant cost savings and efficiency improvements. However, for some processes, particularly in the Finance and Accounting (F&A) function, the extent of automation achieved has been restricted by the limitations of RPA in processing unstructured data.

In this paper, we discuss the role of Intelligent Document Processing (IDP) in addressing this challenge, by extracting meaningful data from large volumes of semi-structured and unstructured documents through Artificial Intelligence (AI). In F&A, we examine the process efficiency improvements that can be achieved by the adoption of IDP, along with RPA, in processes that involve data extraction from documents such as invoices, orders, and expense receipts.