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 a variety of 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 formats that are structured, pertinent, and usable for crucial processes including government, banking, insurance, orders, invoicing, and loan processing forms. IDP gathers the required data and forwards it to the appropriate department or place further along the line to finish the process.
Organizations can digitize and automate unstructured data coming 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 abilities in document identification, contextualization, and processing 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 services, 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 and 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 in the absence of 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 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, companies either relies on its internal data or outsource the collection requirement to third party vendor like TagX who can handle the whole collection process for a specific IDP usecase.
Pre-processing
Intelligent document processing (IDP) can only produce trustworthy results if the data it uses is well-structured, accurate, and clean. Because of this, intelligent document recognition software cleans and prepares the data it receives before actually extracting it. For that, a variety of techniques are employed, ranging from deskewing and noise reduction to cropping and binarization, and beyond. During this step, IDP aims to integrate, validate, fix/impute errors, split images, organise, and improve photos.
Classification & Extraction
Enterprise documentation typically has multiple pages and includes a variety of data. Additionally, the success of additional analysis depends on whether the various 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 categorised material, such as dates, names, or numbers. Large volumes of subject-matter data are used to 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 at the post-processing phase. To guarantee the accuracy of the processing results, the extracted data is subjected to a number of automated or manual validation tests. The collected data is now put together into a finished output file, which is commonly in JSON or XML format. A business procedure or a data repository receives the file. IDP can anticipate the optimum course of action. IDP can also turn data into insights, automation, recommendations, and forecasts by utilising its AI capabilities.
Top Use Cases of Intelligent Document Processing
Invoice Processing
With remote work, processing bills has never been simpler for the account payable and human resources staff. Invoice collection, routing, and posting via email and paper processes results 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 that leads to delay and inaccurate payment. However, intelligent document processing makes sure that all information is gathered is in an organised 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 suffer with data processing 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 convoluted workflows, sluggish processing, high expenses, increased mistake, and fraud. Both insurers and clients must wait a long time during this entire manual process. However, intelligent document processing is a cutting-edge method that enables insurers to swiftly examine the large amount of 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 utilising AI technologies like OCR and NLP.
Fraud Detection
Document fraud instances are increasing as a result of the processing of a lot of data. Additionally, the manual inspection of fraudulent documents and invoices is a time-consuming traditional procedure. Any fraudulent financial activity involving paper records may result in diminished client confidence and higher operating expenses. Therefore, implementing automated workflows for transaction validation and verification is essential to preventing fraudulent transactions. Furthermore, intelligent document processing has the ability to automatically identify and annotate questionable transactions for the fraud team. Furthermore, 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 to read unstructured data from many sources, which eliminates the need for manual processing and saves countless hours of work. 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 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 effectively manage a patient’s whole medical history and file. Many hospitals continue to save patient information in manual files and disorganised paper formats that are prone to being lost. So it becomes a challenge for a doctor to sort through all the papers in the files to find what they’re looking for when they need to access a 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, smart way, it all heavily relies on three cornerstones: Artificial intelligence, optical character recognition, and robotic process automation. Let’s get into a bit more detail on 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 simply “sees” what’s there 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 deals with designing, training, and deploying models that mimic human intelligence. AI/ML is used to train the system to identify, classify, and extract relevant information using tags, which can be linked to a position or 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)
CV utilizes deep neural networks for image recognition. It identifies patterns in visual data say, document 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)
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 key data points.
Robotic Process Automation
RPA is designed to perform repetitive business tasks through the use of 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 certain sources, process and manipulate data, and communicate with other systems. Most importantly, since RPA bots are usually rule-based, if there are any changes in the structure of the input, they won’t be able to perform a task.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
It is needless to say; the number of such documents will keep on piling up and making it impossible for many organizations to manage effectively. Organizations should be able to make use of 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 powerful even in varying formats and also helps organizations to ensure enhanced productivity and operational efficiency.
The implementation of IDP is not as easy. The big 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 into document processing automation by relying on third-party vendors like Tagx who can help you with the collection, classification, Tagging, and data extraction. The more processes you automate, the more powerful AI will become, enabling it to find ways to automate even more.
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