Category: Blog

Intelligent Document Processing

How Can Intelligent Document Processing (IDP) Help Organisations Succeed?

Intelligent Document Processing (IDP) converts manual forms into a digital format to integrate these documents into business processes. This technology exists to help organisations to save time, save money, and to reduce errors while processing and digitising documents.  IDP is a viable way for companies to increase the capacity of their existing workforce, allowing them to handle more without increasing the headcount. As almost every organisation is mandated to do more with less, IDP is a critical technology that can help organisations thrive. Although IDP sounds like the latest innovation in the AI space, the IDP industry has evolved over the last 30 years. In the early days, it used Optical Character Recognition (OCR) solutions to convert letters and characters in the images into machine-encoded text. Today, next-generation solutions can interpret natural language, incorporate computer vision, and advance machine learning. The next-generation IDP solutions play nice and integrate with a whole stack of enterprise applications. The benefits of intelligent document processing technology are almost endless,  Reduce costs Increase productivity  Improve security  Saves time  Eliminate manual processes  Automate repetitive tasks  Create custom workflows  Streamline business processes and much more!  In the upcoming section, we will focus on a few of the benefits of using IDP.  Five Ways Intelligent Document Processing Technology Helps Businesses Succeed 1. Automate document processing tasks Many business processes are automated by intelligent document processing technology, including scanning, OCR (Optical Character Recognition), indexing, tagging, and data extraction. These technologies help businesses in saving time and money while increasing productivity. 2. Improves Customer Service Intelligent document processing technology enables businesses to provide customers with faster and more accurate responses. Documents are automatically indexed and tagged when scanned, making them searchable and accessible. Companies can then respond to customer inquiries without manually reviewing each document. 3. Reduce Costs Companies can use Intelligent Document Processing technology to reduce paper-based operations and eliminate the need for manual labour. Furthermore, Intelligent Document Processing minimises the time spent on repetitive tasks, resulting in greater efficiency. 4. Optimise Workflow Intelligent document processing software makes workflow management easier and eliminates the need for employees to perform mundane tasks such as copying and entering data from business documents and forms. By integrating with existing systems, Intelligent Document Processing technology increases the speed at which documents and data are processed to streamline your business operations. 5. Improves Accuracy IDP solutions eliminate the need for manual processing of data. Hence, there is no room for human error, and the data extracted is >99% accurate. IDP ensures that data extraction processes are quicker and more accurate than before.


Intelligent Document Processing: A Detailed Guide

Did you know more than  80% of enterprise data is unstructured? Yes, you read that right. A common challenge most companies face is a large volume of data that goes unprocessed daily due to its unstructured or semi-structured nature.  While there are traditional methods of processing data, they have proven to be expensive, time-consuming, and prone to many errors. Hence, companies have now turned to Intelligent Document Processing (IDP).  You must wonder what Intelligent Document Processing is; let us explore that in the following sections.  What is IDP?  Intelligent Document Processing (IDP) eliminates the need for manual data extraction by replacing it with machine learning and AI-based technologies. With the help of these technologies, IDP extracts data from documents automatically and converts semi-structured or unstructured data to structured data that companies can use for various purposes.  To automatically extract data, IDP uses different solutions such as Computer Vision, Optical Character Recognition (OCR), Natural Language Processing (NLP), Human in the Loop, and much more. Apart from data extraction, IDP also ensures that the extracted data is categorised, classified into relevant categories, and is simultaneously validated.  More often than not, IDP is often mistaken for OCR and vice versa. However, this is not true. Let us look at the differences between IDP and OCR to help us differentiate between the two.  Before we understand the differences between OCR and IDP, let us look at the various ways data extraction takes place. There are three ways in which data can be extracted — Manual Data Extraction, Optical Character Recognition (OCR), and Intelligent Document Processing. The least preferred of the three is manual data extraction, which proves to be cumbersome and prone to many errors. The following preferred choice is optical character recognition. You must wonder what optical character recognition is; we will give you a brief explanation below.  What is OCR? Optical character recognition assists in converting a scanned image into text by transcribing each character found on the image. In short, OCR extracts the text it detects on an image and converts it into readable information for its user.  There are two kinds of OCR — template-based OCR and zonal OCR. With template-based OCR, you can easily extract any information from a template-based document. On the other hand, the zonal OCR detects blocks of text or a ‘zone’ of the document and extracts data from the same. However, there is a downside to using OCR for data extraction.  While OCR works well with a template-based document, any slight variation in the template leads to an unsuccessful data extraction through OCR. OCR’s limited scope cannot cater to semi-structured and unstructured documents. To help overcome these limitations, companies have turned to IDP. Now, let us understand the differences between OCR and IDP.  OCR vs IDP The key differences between OCR and IDP are as follows: Key Differences  OCR IDP Scope Caters to template-based documents.  Can extract and process data from template-free and complex documents. Accuracy Data processed by OCR has to be manually verified as it can be prone to errors. IDP is almost error-free as it uses HITL technology to validate the data extracted automatically. Automation Cannot extract data fromhandwritten documents or legal documents such as contracts. IDP uses AI technology toadapt itself to extract data from multiple templates and layouts. Capability OCR is limited to data extraction; it does not perform any additional functions. IDP extracts data and classifies the extracted data into relevant categories and simultaneously validates it. As you can see from the above table, Intelligent Document Processing has proven to be more efficient than traditional OCR. However, despite its limited functionality, traditional OCR should not be overlooked. Many companies use OCR combined with Intelligent Document Processing solutions to achieve optimal results. Now let us explore the benefits of IDP.  Benefits of IDP  We have a fair overview of Intelligent Document Processing and how it works; let us now examine the key benefits of using Intelligent Document Processing solutions. The key benefits of IDP are as follows:  Cost-Effective  Running on a fixed budget is crucial for any business. However, document processing can prove to be expensive as it requires a large number of people. With Intelligent Document Processing solutions, you can eliminate the need for manual verification as they assist you in quickly processing, classifying, and validating data. Hence, as an organisation, if you choose to employ IDP solutions, you are bound to reduce the cost of document processing by 50% or more in a year.  Quick Processing  Data extraction can get painfully excruciating when there is a large volume of data that has to be processed. It is because manual data processing can be time-consuming and cumbersome. However, Intelligent Document Platform platforms and solutions have tackled this issue by employing AI and machine learning technology to process and classify data quickly. With IDP, you can save about 50% to 70% of document processing time.  Improved Accuracy As mentioned above, IDP solutions eliminate the need for manual processing or verification of data. Hence, there is no room for human error, and the data extracted is almost 99% accurate. IDP ensures that data extraction processes are quicker and more accurate than before.  Scalable Solutions  A common challenge with manual data processing or traditional OCR is that they cannot extract data from various sources such as handwritten documents, receipts, contracts etc. IDP solutions tackle these issues as it uses its AI and machine learning capabilities to adapt itself to extract data from various sources and documents of different sizes and layouts. It not only extracts data but also interprets the information found on a specific document.  Enables Automation  Most companies lose a fair amount of time performing mundane day-to-day tasks. Everyday tasks can get monotonous and may even disrupt the workflow. With Intelligent Document Processing platforms, you can help simplify data processing by automating daily, mundane tasks integral to a smooth business workflow.  Enhances Quality of Data  Almost 80% of data is unstructured, making it inaccessible. Yet another benefit of Intelligent



Time to drop traditional data processing and adapt “intelligent” solutions

These days, no organization can function without data. Data is the fuel that powers businesses since enormous volumes of data are produced every second from corporate transactions, sales numbers, customer logs, and stakeholders. All of this information is compiled into a sizable data set. It has its own unique difficulties. To improve decision-making, this data needs to be examined. However, businesses still face certain difficulties with large volumes of data. These include issues with data quality, storage, a dearth of data science experts, validating data, and gathering data from various sources, this will lead to bottlenecks in business processing and challenging to take decisions based on the data for further growth of the company. According to various estimates, 80% of enterprise data is semi-structured or unstructured, making it challenging to automate processes using conventional automation technologies. Enterprises are increasingly required to process massive amounts of semi-structured and unstructured materials more accurately and quickly. RPA can automate data from legacy, third-party, and web apps (surface automation), but it does not work well with unstructured data sources (e.g., documents, emails, and attachments). Simply put, unstructured data is information that cannot be easily stored in a standard relational database and is not organized in accordance with a pre-established data model or schema. So how do businesses deal with processing unstructured data in processes that are document-centric? Despite the fact that optical character recognition (OCR), whose accuracy with legacy OCR is only about 60%, aids in the digitalization of paper-based information assets, its inherent quality problems are difficult to ignore. For further processing by RPA or other downstream systems, intelligent document processing (IDP) solutions can process semi-structured & unstructured data and transform it to structured format in this situation. Data comes in various formats: Structured Semi-structured Unstructured Volume: this data is generated constantly Velocity: you need to process them quickly Variety: many sources and data types are used Veracity: data must be of good quality  The IDP software market is expanding quickly All vertical industries’ organizations continue to rely heavily on papers as a source of data input. The unstructured data in these documents necessitates knowledge workers for manual data entry, exception management, and quality checks, which makes document processing labor-intensive, time-consuming, and expensive. Large, small, and medium-sized businesses all spent roughly $400 million on IDP software in 2018, and that amount rose to about $550 million in 2019. According to Everest Group projections. It is simple to see how the unorganized document processing market for machine learning (ML) solutions is big enough for bundled IDP solutions to gain traction. Some of the main use cases for IDP solutions are Know Your Customer (KYC), invoice processing, insurance claims, patient onboarding, patient records, proof of delivery, and purchase forms. IDP software is useful in business-specific procedures including trade financing, mortgage processing, customer onboarding, and the preparation of legal papers. Given its high volume and proneness to error, accounts payable and accounts receivable are frequent use cases for IDP in the financial and accounting industry. Document automation is slowed significantly by the need to create templates In general, users of IDP software should only require a minimal amount of training for template updates. However, businesses who work with hundreds to thousands of vendors each month are aware that updating invoice templates is a time-consuming procedure. The amount of consultation time required to set up and use templates for different sorts of documents can drastically increase total costs. In such circumstances, it is simple to see how an IDP without templates can drastically lower total cost of ownership (TCO) and enable a quicker time to automation. There is no need to wait for months to create templates, let alone actual documents. IDP solutions serve as an intelligent automation tool with a specific purpose Simply said, intelligent automation integrates RPA and document capture and processing capabilities with artificial intelligence (such as natural language processing, machine learning, and computer vision). IDP solutions are utilized to ingest unstructured data into workflows for end-to-end automation, and AI/ML capabilities are leveraged to increase straight-through processing (STP) with accuracy. Automated data verification and validation as well as ongoing learning and improvement based on AI/ML algorithms and user inputs are made possible by pre-built AI/ML capabilities and business rules. IDP automates the retrieval, comprehension, and integration of documents needed for carrying out a business process by combining OCR, data capture, and AI/ML. End-to-end process automation is possible when RPA, IDP, and APIs are utilised in conjunction. IDP enables data-led automation of documents including unstructured and semi-structured data, in contrast to RPA’s focus on processes.