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Intelligent Document Processing (IDP) for unstructured data: An ultimate guide

Rahul Bishnoi
Marketing Manager

With the advent of machine learning and artificial intelligence, Intelligent Document Processing (IDP) has emerged as a game-changer for industries handling vast amounts of paperwork, such as mortgage, insurance, and banking. But what is IDP? How does it work, and how can it benefit your business?

What is Intelligent Document Processing (IDP)?

Intelligent Document Processing (IDP) is a technology that combines artificial intelligence, machine learning, and natural language processing to automatically capture, process, and extract relevant information from unstructured data sources such as documents, emails, and images. Unlike traditional methods that require manual data entry or simple character recognition, IDP is capable of understanding the context and meaning of the information, making the data extraction process more efficient and accurate. It's widely used in industries that handle large volumes of documents, such as finance, healthcare, legal, and human resources.

IDP solutions streamline and automate document processing tasks, reducing manual labor and minimizing errors. As per Gartner's review, IDP technologies offer promising advancements in streamlining business processes, increasing efficiency, and reducing operational costs.

IDP vs OCR: A Comparative Analysis 

Optical Character Recognition (OCR) technology, which extracts text from images, has been a stepping-stone for document digitization. However, OCR falls short when dealing with complex, unstructured documents as it lacks understanding of the context.

On the other hand, Intelligent Document Processing extends beyond OCR capabilities, by using advanced AI technologies. Not only does IDP extract data, but it also understands the meaning and context of the extracted information. This facilitates seamless handling of varied, complex documents – a feature imperative in today's dynamic business environments. This significant leap in document processing is why many organizations are transitioning from OCR to IDP.

What are the Technologies used in IDP?

Robotic process automation, optical character recognition, and artificial intelligence are the three main building blocks for innovative, intelligent document processing. Let's explore each technique in more detail.

  • Artificial Intelligence (AI): AI is a broad term that refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, problem-solving, perception, and language understanding. In the context of IDP, AI is used to emulate human reading and comprehension abilities, enabling the system to understand and process unstructured data found in various documents.
  • Machine Learning (ML): ML is a subset of AI that involves the use of statistical techniques to enable machines to improve at tasks with experience. Machine learning models are trained on large amounts of data, allowing them to learn patterns and make predictions or decisions without being explicitly programmed to do so. In IDP, machine learning algorithms can be used to classify documents, identify key entities and extract relevant information based on the patterns they've learned during training.
  • Natural Language Processing (NLP): NLP is a field of AI that focuses on the interaction between computers and humans through language. It allows machines to understand, interpret, and generate human language in a valuable way. NLP involves several sub-tasks, including language understanding, language generation, translation, and text summarization. In IDP, NLP is used to understand the context and semantic meaning of the text within documents, enabling more accurate extraction and classification of the data.

How Does Intelligent Document Processing Work?

The operation of an IDP system is usually a four-step process:

  1. Data Capture: In the first stage, the IDP system captures data from various sources. The documents can be in different formats, such as PDFs, emails, scanned images, and more. Advanced IDP solutions can handle a wide range of input formats and sources.
  2. Data Classification: Once the data is captured, the IDP system uses machine learning and AI algorithms to classify the documents based on their content and structure. This classification aids in managing and sorting a vast amount of data efficiently.
  3. Data Extraction: After classification, the IDP system proceeds to extract relevant data from the documents. It employs OCR (Optical Character Recognition) to convert different types of documents into machine-readable text. However, unlike traditional OCR systems, IDP solutions use AI and NLP to understand the context and extract meaningful information from the unstructured data.
  4. Data Validation and Export: The final stage involves validating the extracted data. The IDP system checks the data for potential errors or inconsistencies. If the system flags any discrepancies, they are reviewed manually. Once validated, the data is exported to a database or a designated business application.

What are the benefits of Intelligent Document Processing Solution? 

IDPs application in industries like mortgage, insurance, and banking, where document processing is a primary task, offers numerous benefits:

  • Improved Efficiency and Productivity: By automating the process of data extraction from unstructured and semi-structured documents, IDP solutions significantly reduce the time and effort required for manual data entry, leading to increased efficiency and productivity.
  • Cost Savings: Automating the document processing tasks reduces the need for a large workforce dedicated to data entry and verification, leading to substantial cost savings.
  • Enhanced Compliance: IDP solutions can be programmed to follow specific rules and regulations, helping businesses maintain compliance in regulated industries such as finance, healthcare, and insurance.
  • Improved Customer Service: Faster data processing can lead to quicker response times to customer inquiries and improved overall customer service.

What are the top use cases of Intelligent Document Processing?

  • Invoice Processing: Businesses receive invoices in different formats from different vendors. IDP can be used to automate the extraction of key data points from these invoices, such as the vendor name, invoice date, invoice amount, and line item details. This can significantly speed up the invoice processing time and reduce errors.
  • Contract Analysis: Legal departments often have to deal with a large number of contracts. IDP can be used to extract key details from these contracts, such as parties involved, contract dates, terms and conditions, and obligations. This can help in contract management and compliance.
  • Insurance Claims Processing: Insurance companies deal with a large number of claims, which often involve processing multiple documents. IDP can be used to extract information from these documents, speeding up the claim processing time.
  • Bank Statement Analysis: Banks often need to process bank statements for loan processing. IDP can be used to extract financial data from these statements, speeding up the loan approval process.

How is IDP useful in Mortgage, Insurance, and Banking Industries?

Intelligent Document Processing (IDP) can be highly beneficial for industries such as mortgage, insurance, and banking, which often deal with high volumes of complex, unstructured documents. Here's how:

  • Mortgage Industry: The mortgage process involves numerous documents, including loan applications, credit reports, appraisal reports, title reports, and more. IDP can automate the extraction of data from these documents, significantly speeding up the loan approval process. It can also help in identifying and extracting key information for compliance checks and risk assessment, reducing the chances of errors and improving the overall accuracy of the process.
  • Insurance Industry: Insurance companies deal with a variety of documents such as policy applications, claim forms, medical reports, and more. IDP can help in extracting information from these documents, speeding up the policy issuance and claim settlement processes. It can also help in detecting fraudulent claims by identifying anomalies and discrepancies in the documents.
  • Banking Industry: Banks deal with a large number of documents for various processes such as account opening, loan processing, customer KYC, and more. IDP can automate the data extraction from these documents, reducing manual effort and errors. It can also help in compliance checks by identifying and extracting key information from the documents.

What are the Limitations and Challenges of IDP?

Intelligent Document Processing (IDP) has many strengths, but it also faces several challenges and limitations, particularly when dealing with certain types of documents. Here are some of the key challenges:

  • Unstructured Data: One of the biggest challenges for IDP systems is dealing with unstructured data. Unstructured data lacks a predefined format or organization, making it difficult for machines to understand. Documents with varying layouts can pose a challenge for IDP systems, which may struggle to accurately identify and extract relevant information.
  • Handwritten Documents: While IDP has made significant strides in reading typed text, handwritten text still poses a considerable challenge. Handwriting can vary significantly from person to person, and even the same person's handwriting can change over time. This variation can make it difficult for IDP systems to accurately read and extract data from handwritten documents.
  • Poor Quality Documents: Poor quality documents, such as scanned documents with low resolution, poor lighting, or physical damage, can significantly affect the accuracy of IDP. Noise and distortion in the document can make it difficult for the system to accurately recognize and interpret the text.

Despite these challenges, there are strategies that can be used to improve the performance of IDP systems:

  • Training on Diverse Data: By training the machine learning models on diverse data that includes a wide variety of document types, formats, and quality levels, the models can become more robust and better able to handle different scenarios.
  • Preprocessing Techniques: Preprocessing techniques such as noise reduction, binarization, and skew correction can be used to improve the quality of the input document before it is processed by the IDP system. This can help improve the accuracy of text recognition.
  • Human-in-the-loop (HITL): In a HITL system, humans and machines work together, with each doing what they do best. The system can process the bulk of the data, and when it encounters a document that it is unsure about, it can flag it for human review. This can help improve the accuracy of the system and also provides additional data for training and improving the machine learning models.


The future of Intelligent Document Processing (IDP) is promising and expected to bring about significant changes in the way businesses handle unstructured data. Here are some ways in which IDP might evolve and its potential impact:

  • Improved Accuracy: With advances in machine learning and artificial intelligence, the accuracy of IDP systems is likely to increase. As these systems get better at dealing with unstructured data, handwritten text, and poor-quality documents, businesses will be able to automate more of their document processing tasks, leading to increased efficiency.
  • Data-Driven Decision Making: With IDP, businesses can turn their unstructured data into structured data that can be easily analyzed. This could lead to more data-driven decision making, as businesses will have access to more accurate and up-to-date information.
  • Real-Time Processing: Future IDP systems may be capable of real-time document processing. This means that as soon as a document is received, the system can immediately process it and extract the relevant data. This can greatly reduce the time it takes to process documents and make decisions based on the extracted data.

Vaultedge Doc AI: Your Ultimate IDP Solution 

With the growing IDP market, finding the right vendor can be challenging. Vaultedge DOC AI, a leading IDP solution, stands out due to its AI-driven, scalable, and customizable platform. Its ability to process a wide variety of document types, high extraction accuracy, and seamless integration with existing systems make it a viable choice for businesses seeking to adopt IDP technologies.

It offers unparalleled efficiency in transforming unstructured data into actionable insights, thereby resolving major business challenges. Whether it's for insurance underwriting, claim processing, or commercial underwriting, Vaultedge DOC AI simplifies and automates the document handling process, helping businesses operate smarter and faster.

As we advance further into the digital era, technologies like Intelligent Document Processing will become increasingly vital in managing data-heavy processes. The ability to automate complex tasks with high accuracy and efficiency makes IDP an essential tool for any business that seeks to stay competitive in today's rapidly evolving technological landscape.

Vaultedge is committed to this journey, providing robust IDP solutions that cater to your current and future needs. We believe in the power of intelligent automation, and we strive to make it accessible and beneficial to all. Partner with Vaultedge to unlock the full potential of Intelligent Document Processing for your business.

Rahul Bishnoi
Marketing Manager