Generative Pre-trained Transformers (GPT) have brought powerful AI capabilities to the common user, making a significant impact on how enterprises handle unstructured data. One such area of interest is Intelligent Document Processing (IDP), a concept that has been around for a while now. In this article, we will introduce IDP, discussing its origins, core concepts, potential value, and practical considerations for investment as a digital enabler.


The history of Intelligent Document Processing (IDP) dates from the 19th century, with the development of Optical Character Recognition (OCR) by Emanuel Goldberg. This early form of OCR was designed to convert characters into telegraph code and had a specialized purpose.

In the 1970s, the first omni-font OCR system was created, leading to widespread use of traditional computer vision algorithms in the 1980s due to advances in scanning and optical technology. From the early 2000s, OCR capabilities evolved with the integration of recognition algorithms and machine learning models, now available through major cloud services. This represents a significant advancement in OCR technology.

Recent developments in natural language processing (NLP) and deep learning Transformer models, like Large Language Models (LLM), indicate a shift towards more sophisticated document processing tools capable of understanding data meaning beyond text and structure. 

What is IDP?

IDP is a systematic approach to handling unstructured data within documents on large scale. It yields structured and trustworthy data that can be effectively leveraged in downstream process and reporting activities.

From a technological point of view, we consider IDP as an umbrella term for various activities related to the automated extraction, interpretation, classification, and handling of digitized documents. IDP solutions typically use machine learning (ML), deep learning, computer vision (OCR), and natural language processing (NLP) technologies to extract and interpret data from structured and unstructured content, to support automation and augmentation of business processes. 

The Intelligent Document Processing Flow

A high-level IDP processing flow example is as follows;

  • Data Ingestion: The data that needs to be processed is analyzed, explored, and ingested for processing. The initial steps of the project will start at this stage and might require some manual iterations to get collect all the right data. However, going forward this step mostly becomes an integration that sends the files straight into processing.
  • Pre-processing & OCR: After the collection of the documents, they are sent through various pre-processing steps that clean the imperfections from the documents, preparing them for extracting the content via OCR and computer vision. These extract the content on character level, leveraging heuristics and models to keep high accuracy under non-ideal circumstances.
  • Document Classification: Diverse document types might come in and require different handling. The classification of documents is a challenge on its own as this isn’t always clear cut. Classifying the documents helps to organize the data and makes it easier to work with downstream. For example, we might want to classify based on the language, the layout, or the content of the document.
  • Information Extraction: Information is extracted from documents in what we normally refer to as “entities”. They might involve things like specific numbers, dates, or identifying key phrases or topics within a document, or even as ‘complex’ entities, mapping to ontologies within a specific domain. The goal is to turn the documents into structured data that can be used to automate or to gain insights.
  • Validation: After data is extracted, it's important to make sure that it is accurate, consistent, and complete. In edge cases this might require a ‘Human-in-the-loop’ check, but overall, this consists of configurable business rules that are executed on the now structured data set. We want to ensure that we can trust data before it gets ingested in downstream systems for insights and automation.
  • Data Enrichment: As an advanced case of validation, it can also be enriched and crosschecked with live internal systems, business rule engines or models. This typically involves sending the extracted data to an internal or external API endpoint, including the response in the final processing result.
  • Integration: The result of the structured and ‘interpreted’ document is finally sent to the relevant system(s) for further processing. It may trigger further (automated) business processes, or feed reporting and insight flows. Information that was previously manually used for a very specific purpose, can now be leveraged throughout the company.

The “Human-in-the-loop”

For some of the steps mentioned, a ‘human-in-the-loop’ flow might be required. This is typically the case for activities that require expert decision-making or interpretation. Machine learning models are typically used here, validating and correcting these models requires a close feedback loop that feeds those validations and corrections back into the model. This enables continuous learning, required for higher accuracy in the model, which should lead to a higher level of trust in the solution.

Key Drivers of Intelligent Document Processing

The adoption of Intelligent Document Processing (IDP) is driven by a combination of factors. We identify the following critical drivers underpinning the rise of IDP in modern business practices:

  • The explosive growth of digital documents in the digital age requires efficient management, and IDP answers the call. It's more than just a time-saver; it's a cost-effective operational efficiency booster.
  • In today's data-driven landscape, IDP plays a crucial role in extracting meaningful insights from unstructured data, including handwritten notes and unconventional formats. This, in turn, empowers organizations to make informed decisions based on concrete data.
  • IDP can act as a safety net, significantly reducing risks associated with unstructured data processing through automation and precision, ultimately enhancing compliance and averting costly errors.
  • The mantra in the business world is clear: "Time is money, and the customer is king." IDP streamlines document-related processes, reducing the time and effort required. This operational enhancement not only leads to cost savings but also ensures quicker, more customer-centric services.

The Value of Intelligent Document Processing

IDP streamlines document-based processes, leading to different value streams for an organization:

  • It enables process standardization by minimizing errors and digitizing process data, positively impacting efficiency and operational costs.
  • IDP accelerates the digital transformation journey of organizations. It enables the digitization of paper-based documents, making their contents easily accessible and searchable in a structured format.
  • By automating and streamlining document-related tasks, IDP empowers employees to direct their efforts toward high-value activities.

When considering an IDP approach, it is important to recognize which value streams are most relevant and valuable for your specific organization and/or use cases. This will then influence the KPIs that measure success. 

Why invest now in IDP as a technology?

Recent tech advancements have paved the way for widespread IDP adoption.

  • OCR has become ubiquitously accessible, thanks to its commoditization on major cloud providers like AWS, Azure, and GCP.
  • Natural Language Processing technology has also become more attainable with the rise of GPT models redefining AI-powered text interpretation.
  • The ongoing momentum of process automation and orchestration, including Robotic Process Automation (RPA) and Business Process Management (BPM), aligns perfectly with IDP, structuring data that was previously not available for process automation purposes.
  • Today’s IDP technologies achieve increasingly remarkable results with fewer documents, saving both time and resources when setting up document processing flows.
  • 'Low code' IDP platforms make these powerful technologies accessible to business experts, reducing the need for complex data science and engineering projects for many use cases.

Which IDP platform fulfills your needs?

Within KPMG Lighthouse, we have an IDP Vendor Assessment Framework to assess the overall quality and efficiency of IDP solutions, including their ability to handle diverse formats, ease of training and finetuning, support for "human in the loop" workflows, user experience, etc. Adjusting the weights of this assessment to an organization’s context and ambitions, enables us to choose the right tooling in line with your immediate needs as well as your future roadmap.

We support clients end-to-end, from vendor and use assessment to the implementation of the IDP platform and models, including change management of the impacted business users and processes.


Authors: Jonas Vanden Branden & Yasser Barona Dao