Emerging Tech Development & Consulting: Artificial Intelligence. Advanced Analytics. Machine Learning. Big Data. Cloud
According to Statista, the total enterprise data volume will double by 2022 worldwide, reaching more than 2 petabytes. And about 80% of this data will be unstructured (think: email, imaging, and other data that cannot be analyzed as is). Undeniably valuable sources of insight, the ever-growing volumes of unstructured data present a problem, though: handling it is less than rewarding for the human workforce.
So, how do enterprises make the data they have drive real business value, and how do they do it without overburdening the employees?
Intelligent document processing, or IDP for short, might be an answer.
While the “document processing” part of the term needs no explanation — translating document content into meaningful and actionable insights is what businesses have done for ages, the “intelligent” part makes all the difference. It stands for leveraging technologies, such as optical character recognition and artificial intelligence, including natural language processing and computer vision, for automating document processing.
What IDP solutions do in particular is transform unstructured and semi-structured data into the information fit for machine processing, classify and validate this data, and extract insights from it.
Intelligent document processing solutions are particularly relevant for analog, document-heavy industries, like insurance, healthcare, banking, and finance. They typically deal with high volumes of unstructured data, such as invoices, sales orders, and customer correspondence, which are not easily analyzed manually or rule-based automation software.
There are several steps a document goes through when processed with IDP software. Typically, these are:
1. Data Collection
Intelligent document processing starts with ingesting data from various sources, both digital and paper-based. For taking in digitized data, most IDP solutions feature built-in integrations or allow developing custom interfaces to enterprise software. When it comes to collecting paper-based or handwritten documents, intelligent document recognition technology integrates with hardware, like scanners, to speed up the scanning process.
The outcomes of intelligent document processing are only reliable as long as the data IDP relies on is structured, accurate, and clean. That is why before actually extracting data, intelligent document recognition software cleans and grooms the information it’s being fed. For that, many different techniques come to use, from noise reduction and deskewing to cropping and binarization and beyond.
Enterprise documentation is usually multi-page and features different types of information. And the success of further analysis depends on whether different types of data featured in a document go to the right processing workflow.
A loan application, for instance, comprises ID information, bank statements, tax returns, pay stubs, credit history, and other inputs. To classify those inputs, natural language processing algorithms come into play. They break the document down into categories based on the type of the featured content so that only relevant information goes further down the intelligent document processing pipeline.
When it comes to classifying imaging data, the same job is done by computer vision algorithms.
A side note on involving humans in the classification process: intelligent document processing solutions are typically human-in-the-loop. That is, they let a human intervene in the process to validate and improve the work done by the algorithms. So, the automated classification may be followed by selective manual classification carried out by a subject matter expert.
The cornerstone of intelligent document processing, the data extraction stage (quite self-explanatorily), involves extracting insights from the documents. Machine learning models obtain specific information, like dates, names, or figures from the pre-processed and classified documentation.
Machine learning models powering IDP software get trained on vast amounts of subject-matter data. Typically, intelligent document recognition solutions have a library of several models, each tailored to perform in a particular field, like invoices or bank statements.
5. Post-processing and Validation
At the post-processing stage, ML models hone the extracted data, for example, correct common misspellings or adjust the data to match standard formatting.
The extracted data goes through a series of automated or manual validation checks to ensure the accuracy of the processing outcomes.
At this point, the extracted data is assembled into a final output file that is typically in JSON or XML format. The file is passed on to a business process or a data repository via APIs.
The tech pillars powering IDP span optical character recognition (OCR) and artificial intelligence (AI), including such subfields of AI as computer vision (CV) and natural language processing (NPL). Intelligent document processing solutions pair well with robotic process automation (RPA), too. Let’s get into a bit more detail on each technology.
Handling the administrative flow, managing medical records, and ensuring that document processing is HIPAA-compliant, all that while servicing patients, can be daunting. An average physician spends over two-thirds of their time just doing paperwork!
Intelligent document processing can take the draining task of managing hospital documentation off the medical workers’ shoulders. Here’s a rundown of what intelligent document recognition in healthcare is capable of:
From there, IDP solutions can deliver the extracted information to a hospital EHR or any other hospital system.
Manual processing of orders, invoices, and shipping receipts can now become a thing of the past. Intelligent document processing in retail can turn all that hefty information into extraction-ready data that is easier to manage and organize.
The digitized approach to data can help retailers accomplish various tasks — from extracting customer insights for email marketing campaigns to speeding up purchase order processing to timely reacting to customer reviews.
Banking and financial documents are multi-page and usually require a lot of human effort to process. The cost of human error is pretty high, too. Besides, the industry has faced a surge in the number of incoming loan applications, account withdrawal requests, and other services due to the pandemic, so the staff is overwhelmed more than ever.
Intelligent document recognition technology can automatically process bank statements, balance sheets, cash flow statements, tax returns, and other documents, at speed and with minimal risk of errors, so the human effort is reduced to the supervision of the algorithms.
Human resource management can benefit from intelligent document processing, too. The tasks IDP can do for HR employees comprise:
Intelligent document processing solutions can reduce the time it takes to handle insurance documentation from days to hours. The common workflows that can be enhanced via IDP software span:
Intelligent document recognition solutions for insurance are usually trained to extract data inputs from typical documents, such as P&C or life insurance forms, and can be trained to handle exceptional cases.
Managing legal documentation manually is a daunting task distracting legal staff from actually practicing law and servicing their clients. Besides, law firms deal with the documents that have to be archived in the most organized way possible.
Intelligent document processing software can facilitate case reviews, ease up contract administration, as well as help archive digital documents, detect fraud, and protect sensitive client information.
Intelligent document processing lets government employees focus on guiding citizens and driving policies instead of scraping through tons of data.
The applications of IDP in governmental services range from capturing data surveys and gathering online citizen feedback to processing property registration applications, managing permit requests, issuing e-documents, and beyond.
I have my mind on IDP. What do I start my adoption journey with?
If you are about to dive into intelligent document processing, start with assessing your document processing workflows and define the level of workflow automation you’re aiming at.
To understand whether a workflow needs to be automated, assess the number of documents it receives and the time it takes to process them manually.
Identify the workflows that require the ultimate precision, too. They should be added to the IDP implementation pipeline first.
Finally, to make sure that your IDP implementation pays off, do take time to evaluate the complexity of your document processing workflows. Start with breaking your document handling processes down into sub-processes. The more decision-making points between those sub-processes are, the more complex automating the workflows could be, the more resources IDP implementation would require. So, balance the resources you’re about to invest against the value IDP could drive in your organization.
When do I choose a ready-made intelligent document processing solution, and when should I go custom?
The answer to this question depends on the nature of the documents you handle, the complexity of your workflows, and the regulations of the industry you operate in.
The more complex and specific the case, the more likely you are to fail at finding an optimum ready-made solution. On the other hand, if you are about to automate a typical workflow, for instance, processing of support requests, chances are there’s an off-the-shelf solution on the market that can meet your needs.
If I choose to go the custom route, how do I implement IDP at scale?
The best way to adopt intelligent document processing is to go step-by-step. Start with gradually automating a single workflow that is not critical for your business operations. Assess how well the automated process works compared to manual processing and weigh the cost savings the automation drives. If you’re satisfied with the results, you can increase the share of automation within the current workflow and extend IDP to other corporate processes, building on the existing infrastructure.
What roadblocks on the way to IDP adoption should I be aware of?
One of the bottlenecks that may complicate IDP rollout is legacy corporate infrastructure. Outdated enterprise systems can cause IDP solutions to run with lower accuracy and prevent full-on automation of document processing workflows.
Another challenge is a lack of training data. For an artificial intelligence model to operate effectively, it must be trained on large amounts of data. If you don’t have enough of it, you could still tap in document processing automation — but relying on alternative techniques to training AI models, which may take a longer time and more resources.
One more issue that may hinder IDP adoption is employee resistance. They may view IDP software as a replacement for human labor. That is why it is crucial to design your IDP software around end-users, explaining the benefits of intelligent document processing and repurposing human effort to more meaningful, rewarding tasks.
Contact ITRex team if you want to tap into higher operational efficiency at significantly lower costs. We will help you adopt an intelligent document processing solution fast and with minimal risks.
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