Regarded as the backbone every business organization’s workflow, data facilitates growth and improves day-to-day business processes. However, the increasing need to reduce costs, comply with regulations and meet ever-changing customer demands is exerting tremendous pressure on the business processes that rely heavily on data and information. This is especially significant when it comes to dealing with unstructured data trapped inside documents, images, PDFs, and emails. In addition, most companies tend to handle a huge volume of transactions, generating a significant amount of paper documents within that period that may impact their bottom-line.

Overcoming these challenges of extracting, classifying and using this data without employee burnout is a crucial aspect. The significance of Intelligent Document Processing (IDP) becomes evident at this point. With IDP, organizations can transform unstructured data from various document formats. IDP helps optimize document scanning and document conversion services. As the documents are converted to digital format, organizing the information becomes faster and efficient.

What Is Intelligent Document Processing and How Does it Work?

An integral part of any enterprise or organization that uses documents to do business, intelligent document processing (IDP) automates the process of analyzing, extracting, and transforming information from a variety of unstructured document formats. As per definition by Deloitte,”intelligent document processing automates the processing of data contained in documents ― understanding what the document is all about, what information it contains, extracting that information and sending it to the right place”.

IDP uses deep learning AI technology tools like natural language processing (NLP), computer vision, and machine learning (ML) to classify, categorize, and extract relevant information as well as validate the extracted data. The concept of IDP is significant as it provides automation of tasks that would otherwise have to be done manually. This in turn helps organizations to make informed decisions and save valuable time and resources while improving productivity, compliance and accuracy.

Embedding document processing software within the RPA platform is something that enables business users to automate processes from end-to-end. Upon combining IDP and RPA on the same platform, it is possible to have two important pieces of the automation puzzle working in sync. While IDP is a significant component of the automation workflow, RPA brings everything together – right from document download from a source such as an email and sending it for processing. Once, the structured and unstructured data is extracted, the platform facilitates validation and decision-making. The extracted data is then entered into a system of record, completing the workflow.

IDP – Key Components and Stages

Here discussed are some of the key components and stages of intelligent document processing (IDP) –

  • Pre-processing – As documents tend to arrive for processing in various conditions, IDP uses techniques like noise reduction, binarization and de-skewing to maximize the quality of documents.
  • Image processing – At first, IDP uses computer vision to understand document structure and identify”features” such as text, graphs and pictures. Older technologies such as OCR and ICR can then be leveraged to extract text from the document.
  • Classification and data extraction – IDP automatically identifies, separates and classifies document components by using machine learning (ML) and NLP. An IDP’s classification engine is responsible for parsing out all these different components, accurately categorizing them and arranging them as per their next destination. One of the key features of IDP systems is their ability to pinpoint valuable information and extract it for further analysis or processing. To accomplish this, IDPs often include a library of pre-trained extraction models or pattern matching tools such as Regular Expressions (RegEx).
  • Data Validation – To validate data extracted from documents, IDP platforms leverage external databases and pre-configured lexicons. This ensures whether the collected data is of high quality, in the right format and capable for immediate usage. The data validation process typically leverages a HITL (Human-in-the-Loop) machine learning framework, whereby problematic data is routed to humans for correction and review. This approach allows the validation model to continuously learn and improve its accuracy over time.
  • Integration – The last step of the IDP process is to integrate the validated data into large enterprise systems and workflows.
  • Business Intelligence (BI) and Analytics – Business intelligence (BI) and analytics can provide a complete overview of all the bots operating in an environment, offering real-time operational insights such as the number of document processes at work, accuracy rates and information relevant to business outcomes.

Benefits of Intelligent Document Processing (IDP)

Regarded as a powerful tool for organizations, document processing can automate certain tasks, make processes more efficient, and improve the quality of documents. Here discussed are some of the key advantages of using a fully integrated document processing platform –

  • Increased Accuracy – IDP tools can be used to digitally sign or encrypt the documents which ensures that they can’t be altered or tampered with. Using IDP solutions to automate data extraction will boost the accuracy of extracted data and eliminate errors. This in turn will make the document retrieval process more accurate and save time for employees to make educated business choices.
  • Improves Process Efficiency and Effectiveness – Manual data entry is a tedious task that can take several minutes of an employee’s time. IDP can make processes more efficient by eliminating the need for manual data input and reducing human intervention and labor-intensive tasks in many document-centric workflows. IDP tools may take just seconds to extract, convert, sort, and index data. And that includes unstructured documents. The only time manual intervention may be required is if there is an issue with the data – particularly one which IDP solutions can’t fix. This can in turn create a seamless workflow between different departments. In fact, the level of automation that you can get from Intelligent Document Processing solutions increases the overall efficiency of the daily workflow in many businesses.
  • Simplifies Compliance – The impressive accuracy rate feature of IDP makes it an ideal solution for handling any compliance-related document or those that include sensitive information. It automatically detects sensitive information such as personally identifiable information (PII) or health records that need to be protected in all businesses. As IDP eliminates the need for humans to open up, review or handle any of the data included in the documents, it minimizes the risk of exposing sensitive information to outside parties. This in turn prevents data manipulation or misuse as the information is stored in a secure location where only authorized individuals can access it. In addition, IDP can help streamline and maximize the accuracy of regulatory reporting.
  • Higher Productivity – IDP is an innovative software technology that can help business organizations increase productivity and reduce the time spent on repetitive tasks. Document processing is always a time-consuming process. In fact, manual document processing can take up to 70 percent of a worker’s time. But with IDP, managers are able to automate tedious tasks like data entry and record management. Reducing the time spent on repetitive tasks can increase productivity in organizations.
  • Promotes and Scales Automation – One of the significant benefits of IDP is automation as it facilitates end-to-end process improvement. It helps link various systems that go into automating complex business processes and achieving hyperautomation. In addition, cognitive technologies like RPA and AI need structured high-quality data to”learn” from and operate. By transforming unstructured data found in documents into streams of cleaned, sorted and structured data, IDP optimizes data for RPA/AI consumption as well.
  • Faster Document Retrieval – The speed involved in processing and retrieving large volumes of data is another notable benefit of investing in IDP. The technology automatically identifies and extracts textual content from documents, and then classifies, clusters, and links the extracted information with other relevant literature. In this way, it becomes easier to find relevant information within thousands of documents and automate tasks that are usually performed manually (for instance, document classification). With IDP, in-house document processing will yield error-free results and the completion of such a task will be faster as it consumes only a small fraction of one’s time.
  • Enhanced Data Quality and Usability – On an overage, 80 percent of an organization’s data is”dark data” – meaning it’s locked in emails, text, PDFs and scanned documents. By using RPA and AI-based tools, it is possible to unlock the value of dark data by converting it into high quality, structured data that is ready for analysis. As per experts from Mckinsey,”by combining the data derived from paper documents with the wealth of digital data already available, a comprehensive data landscape can be established, significantly enhancing data evaluation and analytics possibilities.”
  • Cost Savings – Reducing the overall costs of day-to-day business operations is an important aspect for any organization. Adopting IDP solutions will not only reduce document processing times (meaning they can cut the need to hire as many employees) but also save on other operational costs. With systems and processes running more efficiently (as mentioned above), it is possible to get a better return on investment (ROI) than with other software or even human employees.
  • Reduced Manual Effort – IDP tools can result in reduced manual effort. The latest developments in AI technology can process documents by extracting information from an image, sound, or video and then transforming it into text. This is usually done through natural language processing (NLP), where the AI can read and understand the content of a document rather than just seeing it as code.
  • Automated Document Classification – Automated document classification is an important aspect in any document management strategy to be implemented. IDP can automatically classify documents and generate distinct categories that will assist organizations in organizing their document collection without the need for human interaction.
  • Easy Integration into Existing Tech Stack – One of the final benefits of IDP is that these solutions can easily be integrated with the existing software and hardware. This means organizations don’t need to spend additional amount to set up new devices or technology to make these work. Instead, they can implement these seamlessly into their existing systems. This makes the implementation of IDP solutions much simpler, easier, and more cost-effective.

As mentioned above, document management is an important aspect in any business and extracting valuable data can help take more informed business decisions.

Intelligent Document Processing (IDP) should be a major component of any document management effort. This technology will create a clear balance between intelligent automation and intelligent people and will continue to improve further by leveraging cutting-edge advanced technologies such as AI and ML. According to a report from Markets and Markets, the global Intelligent Document Processing market size is expected to grow from USD 0.8 billion in 2021 to USD 3.7 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 36.8% during the forecast period. Therefore, businesses need to strike a clear balance between automation and human intervention by focusing more on value-based roles. Relying on the services of a reputable business process outsourcing company can help organizations to stay abreast with the changing technologies and competitors.