Artificial Intelligence has stepped up to the front line of real-world problem-solving and business transformation, with Intelligent Document Processing (IDP) becoming a vital component in the global effort to drive intelligent automation into corporations worldwide.

IDP solutions read the unstructured, raw data in complicated documents using various AI-related technologies, including RPA bots, optical character recognition, natural language processing, computer vision, and machine learning. IDP then gathers the crucial data and transforms it into structured, pertinent, and usable formats for essential processes, including government, banking, insurance, orders, invoicing, and loan processing forms. Finally, IDP gathers the required data and forwards it to the appropriate department or places further along the line to finish the process.

Organizations can digitize and automate unstructured data from diverse documentation sources thanks to intelligent document processing (IDP). These consist of scanned copies of documents, PDFs, word-processing documents, online forms, and more. IDP mimics human document identification, contextualization, and processing abilities by utilizing workflow automation, natural language processing, and machine learning technologies.

What exactly is Intelligent Document Processing?

A relatively new category of automation called "intelligent document processing" uses artificial intelligence, machine learning, and natural language processing to help businesses handle their papers more effectively. Because it can read and comprehend the context of the information it extracts from documents, it marks a radical leap from earlier legacy automation systems. In addition, it enables businesses to automate even more of the document processing lifecycle. 

Data extraction from complicated, unstructured documents is automated by IDP, which powers both back-office and front-office business operations. Business systems can use data retrieved by IDP to fuel automation and other efficiencies, including the automated classification of documents. Enterprises must manually classify and extract data from these papers without IDP. They have a quick, affordable, and scalable option with IDP.

How does intelligent document processing work?

There are several steps a document goes through when processed with IDP software. Typically, these are:

Data collection: Intelligent document processing starts with ingesting data from digital and paper-based sources. For taking in digitized data, most IDP solutions feature built-in integrations or allow the development of custom interfaces to enterprise software. When collecting paper-based or handwritten documents, companies either rely on their internal data or outsource the collection requirement to third-party vendors like TagX, who can handle the whole collection process for a specific IDP use case.

Pre-processing: Intelligent document processing (IDP) can only produce reliable results if its data is well-structured, accurate, and clean. Because of this, intelligent document recognition software cleans and prepares the data it receives before actually extracting it. Various techniques are employed, ranging from deskewing and noise reduction to cropping and binarization. IDP aims to integrate, validate, fix/impute errors, split images, organize, and improve photos during this step.

Classification & Extraction: Enterprise documentation typically has multiple pages and includes various data. Additionally, the success of additional analysis depends on whether the different data types present in a document are processed according to the correct workflow. During the data extraction stage, knowledge from the documents is extracted. Machine learning models extract specific data from the pre-processed and categorized material, such as dates, names, or numbers. Large volumes of subject-matter data train the machine-learning models that run IDP software. Each document's pertinent entities are retrieved and tagged for IDP model training.

Validation and Analytics: The retrieved data is available to ML models in the post-processing phase. The extracted data is subjected to several automated or manual validation tests to guarantee the accuracy of the processing results.

The collected data is now put into a finished output file, commonly in JSON or XML format. A business procedure or a data repository receives the file. IDP can anticipate the optimum course of action. Using AI capabilities, IDP can also turn data into insights, automation, recommendations, and forecasts.

Top Use Cases of Intelligent Document Processing

Invoice Processing

With remote work, processing bills has never been more straightforward for the account payable and human resources staff. Invoice collection, routing, and posting via email and paper processes result in high costs, poor visibility, and compliance and fraud risks. Also, the HR and account payable staff shares the lion's part of their day on manual, repetitive chores like data input and chasing information, leading to delays and inaccurate payment. However, intelligent document processing ensures that all information is gathered in an organized fashion, and data extraction in workflow only concentrates on pertinent data. Intelligent document processing assists the account payable team in automating error reconciliation, data inputs, and the decision-making process from receipt to payment. IDP ensures organizations can limit errors and reduce manual intervention.

Claims Processing

Insurance companies frequently need help processing data because of unstructured data and varying formats, including PDF, email, scanned, and physical documents. These companies mainly rely on a paper-based system. Additionally, manual intervention causes complex workflows, sluggish processing, high expenses, increased mistake, and fraud. Both insurers and clients must wait a long time during this manual process. However, intelligent document processing is a cutting-edge method that enables insurers to swiftly examine many structured and unstructured data and spot fraudulent activity. Insurance companies can quickly identify, validate, and integrate the data automatically and offer quicker claims settlement by utilizing AI technologies like OCR and NLP.

Fraud Detection

Document fraud instances are increasing due to processing a lot of data. Additionally, the manual inspection of fraudulent documents and invoices is a time-consuming traditional procedure. Furthermore, any fraudulent financial activity involving paper records may result in diminished client confidence and higher operating expenses. Therefore, automated transaction validation and verification workflows are essential to prevent fraudulent transactions. Furthermore, intelligent document processing can automatically identify and annotate questionable transactions for the fraud team. Moreover, IDP frees the operational team from manual labour while reducing fraud losses.

Logistics

Every step of the logistics process, including shipping, transportation, warehousing, and doorstep consumer delivery, involves thousands of hands exchanging data. For manual processing by outside parties, this information must be authenticated, verified, cross-checked, and sometimes even re-entered. Companies utilize IDP to send invoices, labels, and agreements to vendors, contractors, and transportation teams at the supply chain level. IDP enables the reading of unstructured data from many sources, eliminating the need for manual processing and saving countless work hours. It also helps to handle the issue of document variability. IDP keeps up with enterprises as they grow and scale to handle larger client user bases due to the intelligent automation of various document processing workflow components.

Medical records

It is crucial to keep patient records in the healthcare sector. In a particular situation, quick and easy access to information may be essential; as a result, it is crucial to digitize all patient-related data. IDP can now be used to manage a patient's whole medical history and file effectively. Many hospitals continue to save patient information in manual files and disorganized paper formats prone to being lost. Hence it became a challenge for a doctor to sort through all the papers in the files to find what they're looking for when they access the specific file. All medical records and diagnostic data may be kept in one location using an IDP, and only pertinent data can be accessed when needed.

The technologies behind intelligent document processing

When it comes to processing documents in a new, innovative way, it all heavily relies on three cornerstones: Artificial intelligence, optical character recognition, and robotic process automation. Let's get into more detail about each technology.

Optical Character Recognition

OCR is a narrowly focused technology that can recognize handwritten, typed, or printed text within scanned images and convert it into a machine-readable format. As a standalone solution, OCR "sees" what's on a document and pulls out the textual part of the image, but it doesn't understand the meanings or context. That's why the "brain" is needed. Thus OCR is trained using AI and deep learning algorithms to increase its accuracy.

Artificial intelligence

Artificial intelligence involves designing, training, and deploying models that mimic human intelligence. AI/ML trains the system to identify, classify, and extract relevant information using tags, which can be linked to a position, visual elements, or a key phrase. AI is a field of knowledge that focuses on creating algorithms and training models on data so that they can process new data inputs and make decisions by themselves. So, the models learn to "understand" imaging information and delve into the meaning of textual data the way humans do. IDP heavily relies on such ML-driven technologies as

• Computer Vision (CV) utilizes deep neural networks for image recognition. It identifies patterns in visual data, documents scans, and classifies them accordingly. Computer vision uses AI to enable automatic extraction, analysis, and understanding of useful information from digital images. Only a few solutions leverage computer vision technology to recognize images/pictures within documents. 
• Natural Language Processing (NLP)finds language elements, such as separate sentences, words, symbols, etc., in documents, interprets them, and performs a linguistic-based document summary. With the help of NLP, IDP solutions can analyze the running text in documents, understand the context, consolidate the extracted data, and map the extracted fields to a defined taxonomy. It can help in recognizing the sentiments from the text (e.g., from emails and other unstructured data) and in classifying documents into different categories. It also assists in creating summaries of large documents or data from charts using NLG by capturing critical data points. 

Robotic Process Automation

RPA is designed to perform repetitive business tasks through software bots. The technology has proved to be effective in working with data presented in a structured format. RPA software can be configured to capture information from specific sources, process and manipulate data, and communicate with other systems. Most importantly, since RPA bots are usually rule-based, they won't be able to perform a task if there are any input structure changes. RPA bots can extend the intelligent process automation pipeline, executing such tasks as processing transactions, manipulating the extracted data, triggering responses, or communicating with other enterprises IT systems.

Conclusion

The number of such documents will keep piling up, making it impossible for many organizations to manage effectively. Organizations should be able to use this data for the benefit of businesses, but when it becomes so voluminous in physical documents gleaning insights from it will become even more tedious. With the use of Intelligent Document Processing, the time-consuming, monotonous, and tedious process is made simpler without any risks of manual errors. This way, data becomes more potent in varying formats and helps organizations ensure enhanced productivity and operational efficiency.

The implementation of IDP is more challenging. The big challenge is the need for more training data. For an artificial intelligence model to operate effectively, it must be trained on large amounts of data. Suppose you don't have enough of it. In that case, you could still tap into document processing automation by relying on third-party vendors like Tagx, who can help you with the collection, classification, Tagging, and data extraction. In addition, the more processes you automate, the more powerful AI will become, enabling it to find ways to automate even more.

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