Transforming Accounting with RPA and Cognitive Automation

Automating accounting has been one of the most critical IT projects within many organizations. The ability to efficiently manage payments for goods and services is critical for financial standing as well as supplier and partner relationships of each organization. So, why are most organizations still relying on manual or semi-automated procure-to-pay processes?

While the scale of this issue varies — in some extreme cases accounts payable (AP) clerks still chase executives with paper invoices to get their signatures — most of the companies use some sort of technology for sending digital invoices via emails or workflow automation systems to gain approvals and tracking financial transactions. Unfortunately, these systems are often disconnected and require manual cross-checks and the pulling of data from one system to another to complete processing. And we cannot blame IT managers for poor integration or limited capabilities of systems. These processes are so complex, dynamic and change so often, that traditional top-down approach to automation driven by business analysts and implemented by IT organization brings only so much value.

Bridging gaps with RPA

Robotic Process Automation (RPA) introduces a different approach to automation where business users, such as accounting managers, are empowered to drive automation of repetitive tasks, while IT implements controls, governance and enterprise grade security, required by the organization. Software robots (also called bots) act as connectors, filling the gaps between systems which were previously handled by human operators. By connecting independent systems, RPA bots do not just streamline processes and enable end-to-end automation, but also simplify and elevate the job of human operators by taking over mundane tasks.

However, even RPA has its limitations. Traditional RPA only handles structured data — data in a database or electronic spreadsheets. But when AP clerks deal with invoices, they are documents, not database tables. We categorize these types of documents as semi-structured, because they have a known set of data fields, but may come in various formats where data cannot be easily pulled. Using automation here requires certain cognitive skills to parse the invoice and put its data into a database of enterprise resource planning (ERP) or accounting system.

Again, if we are looking for end-to-end process automation, which leads to exponentially larger benefits, versus partial automation we need to automate these cognitive tasks. With the development of artificial intelligence (AI) technology, these tasks can also be addressed by cognitive automation tools, such as IQ Bot.

IQ Bot uses a combination of computer vision, optical character recognition (OCR), automatic document analysis, fuzzy logic, and machine learning (ML) to autonomously process documents and extract required information.

To learn more about how RPA and cognitive automation transform accounting, read this whitepaper.

Use ML and RPA to automate
invoice processing.