Use Cases of Intelligent Document Processing
Even though going digital is quickly becoming the norm among many forward-looking companies, the process is often difficult to realize fully. That is because most businesses deal with paper and document-dependent functions at the operational level. Something like a back-office in a typical company meeting with external stakeholders like vendors and customers is always dealing with truckloads of paper.
Back-office paperwork can be cumbersome and time-consuming, and not just because of the sheer volume of paper that piles up with every transaction. Typical document-dependent functions in the back-office like accounts payable and sales order processing involve handling a lot of unstructured and semi-structured documents. Unstructured documents like vendor invoices and receipts are not nice paragraphs of text. The data in these documents is just that: unstructured, meaning a great deal of manual data entry work is needed to get the data from these documents into a business application where most of the work on that data is done.
Many companies rely on line-of-business applications like ERPs, CRMs, and accounting software to get work done. These systems in turn rely on data from incoming transaction or source documents like vendor invoices, credit memos, customer sales orders, statements, receipts, packing slips, etc. All these documents are typically unstructured or semi-structured.
Also, a company may receive data from numerous channels like file shares, MFPs, scan, fax, post, email, etc. Sorting, classifying, and performing data entry work to get information from these documents into a line-of-business application can take up a lot of manual effort, time, and costs. Whenever a company interacts with its external stakeholders, data is generated. This data is mostly unstructured. To perform data entry and get only the required data from unstructured documents into, say, an ERP application takes time, knowledge, and manual effort.
Say, for example, you need to enter the latest vendor invoice details into an accounting system for final reconciliations and to make payments to the vendor. For this, you need to know exactly what details from the invoice to input into the accounting software, like line item details and header-footer information like TOTAL. Also, reconciling vendor payments involves firstly validating the invoice against a matching purchase order, and checking the number of items and TOTAL on both the documents. Other validation requirements may include a lot of business rules like checking the vendor details and making sure the payments for the invoice have not already been made to the vendor. Data validation checks like these are important to eliminate duplicate payments or underpayments to the vendor. A lot of participants are involved in data entry and data validation work. Also, when it comes to approving payments against an invoice, the invoices must be sent to the concerned verification manager for final inspection and approval. All these steps take time, and human error in one or more of these steps can delay processing of invoices from days to weeks.
Traditional capture technology employs OCR to automatically extract details from transaction documents like an invoice, saving companies time on data entry work.
Book a demo now
But this technology is limited in scope by its ability to process diverse document types. This technology is strictly template-based, meaning the software is pre-programmed to locate data from specific regions on an invoice like the bottom right corner for the invoice ‘TOTAL’ and the ‘item list’ in the body section of the invoice, and extract those details. Intelligent document processing , on the other hand, accommodates for document variability.
The intelligent document processing software is pre-programmed to enable data extraction from known invoice formats. Additionally, the software also has self-learning capabilities. That means that in the event the software is required to extract details from a new invoice for which it does not have the built-in capture logic and does not know where in the invoice to locate and extract the required details, it relies on self-learning. Self-learning means the software learns the user keystrokes performed to extract data from a new invoice the first time. The software then retrieves and applies this learning for when it needs to extract data from a similarly formatted invoice document the next time. This way, the intelligent document processing software adds to its growing database of capture logic with every new invoice type, thereby creating a vast database of capture definitions to extract data from a diverse set of document types.
Apart from an elimination of manual data entry work, this technology also dramatically and progressively reduces the number of times a user has to intervene to perform data entry for new document types, as a new capture definition is automatically created through self-learning with every new document type. Intelligent document processing software applies the ‘intelligence’ it acquires to process new document types, thereby expanding the scope and variability of documents it can capture.
Intelligent document processing creates a near-paperless and hands-free back-office. The advantages of adopting this technology for your mission critical work are cumulative and can be seen in many diverse sectors that involve heavy document processing work. We’ll look at a few of them.
Find out how Artsyl fits into your digitization and digital transformation plans and learn how we tackle your everyday document processing challenges intelligently. Contact us today for a product demo of our intelligent document processing solutions.
Lessons from the Pandemic — Banking on Technology
Invoice Processing in the Cloud
Lean Principles and IPA
Bringing Business Back! The Smart Way