Documents such as invoices, purchase orders, checks, receipts, etc. have no standard format. However, businesses need to enter the information from these paper documents into the financial systems and ERPs.
A lot of manual effort is spent in classifying the documents and extracting the data for analysis. As time is limited,
- errors generally creep in, or
- accounting teams are large and more focused on data entry tasks instead of core accounting,
neither of which are ideal options.
RPA can automate some parts of the processing, such as validating the extracted data with on-record information and feeding the data into audit systems. Still, RPAs have limited capacity to automate a large part of the process.
DocVu.AI uses natural language processing to understand and extract the information in the document irrespective of the template. This solves the complexity of having no standard templates for documents like invoices, purchase orders, checks, and receipts which automates the most manual, time-consuming, cost-intensive, and error-prone part of the data entry.
However, today IDPs can identify the documents irrespective of templates, extract the data fields and validate that against the company’s data in the accounting system. IDPs can also integrate into the Accounting system or ERP and directly transfer the extracted data for accounting teams to seamlessly review and execute the next steps. This efficiency reduces the AP cycle times and the effort required to process.