While the age of digitization is transforming the way a business functions, it keeps posing new challenges in front of companies so they can evolve their processes.
An organization receives tons of documents everyday; be it invoices, receipts, loan documents, mails, contracts, and what-not. Document information extraction and processing is the central concern for many organizations.
For instance, in the accounts payable (AP) department, the AP team has to look through thousands of invoices every month. Thanks to the digital era, these invoices share different structures, formats, and layouts, particular to the vendor. Hence, in order to employ automated invoice processing, the AP team needs structured data with a consistent format throughout its lifecycle.
When it comes to invoice data extraction, deep learning has proved to be an intelligent technique to capture structure data from invoices so they can be processed automatically.
Deep learning, in simple terms, is a technique of imitating a human’s decision-making and learning capabilities. Just like humans have the power to adapt to the changing environment and learn by observing changes, deep learning also learns and improves by observing inputs and outputs.
In case of invoice data extraction, deep learning works along the same lines. Since deep learning algorithms depend on data sets to interpret the associations and classification of data to be extracted, the data extraction software has to be trained with significant data sets. This data in simple terms comprises of questions and their possible solutions, so the deep learning algorithm can observe the problem, its solution, and learn to respond accordingly when a similar problem arises.
Moreover, the algorithm learns to understand more complex data sets with time, and hence, becomes efficient and capable of extracting meaningful, structured data from documents (like invoices, purchase orders, expense receipts, loan documents, contracts etc). Deep learning also allows the AP team to determine trends and patterns in the extracted data and draw actionable insights into it. That said, the team is no longer involved in manual data entry or verification, instead, it focuses on extracting value from the available data.
However, deep learning alone cannot make a difference!
When we talk about invoice process automation using AI (artificial intelligence), we are actually referring to a combination of deep learning, computer vision and natural language processing (NLP) techniques.
NLP is a medium of interaction between human language and computers. It helps train machines on how to comprehend a large amount of natural language data that come their way. Given the variability of invoice structure, format, layout and most importantly the text, it becomes imperative to comprehend the text inside the document before invoice data extraction. It would otherwise lead to errors in extracted data and require human verification to rectify them.
Here’s where NLP comes to rescue! It helps the data extraction software to understand the language of invoices, no matter how different it is from standard templates, so it can capture the right key-value pairs by accurately discovering the context. Now this context can be both visual as well as textual. While the visual context is discovered and analyzed using computer vision techniques, NLP helps with the textual context.
The process is an advanced version of traditional OCR-based document scanning and extraction wherein automation was just for namesake.
Traditional OCRs invited more labor to manually enter invoice data into the systems and verify them before sending for approval. When templates were introduced to OCRs, they did solve problems to some extent, but again, posed new challenges in front of the AP team.
The AP staff got busy in creating templates for every new invoice that entered their system. Hence, there was no such thing as an end-to-end automated system for invoice data extraction, until AI and ML integrated with OCR.
With techniques like deep learning, natural language processing, computer vision and API driven automation, invoice processing is no longer human-dependent. When integrated with OCR and data extraction software, they first understand the context of data and interpret the text and then structure it so it can directly be sent for processing and approvals.
Apart from improving the internal business operations, invoice process automation using AI has also reduced AP costs to a significant extent. Moreover, the set-up costs, implementation time, processing time, and payment delays have decreased up to 90%.
If traditional OCRs are no more your thing, switch to automated invoice processing with KlearStack, an AI-enabled data extraction software that combines deep learning, NLP, and ML algorithms to intelligently process invoices.
It is a template-less, end-to-end automated software, designed to improvise internal business processes. KlearStack has helped many businesses level up their document processing strategies and save significantly on their time, money, and resources.
If you are willing to know how KlearStack leverages deep learning for invoice data extraction, book a consultation call with us today, We shall discuss your business case and give you some best implementation practices.