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What is Intelligent Document Processing?

February 27, 2024 | Intelligent Document Processing

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In the world of data management and computational linguistics, a relatively new buzzword has been making the rounds—“intelligent document processing” (IDP). Defined in simple terms, intelligent document processing utilizes artificial intelligence, machine learning, and natural language processing to capture, process, and interpret information from unstructured and semi-structured documents.

Traditional automation tools are effective at processing structured data (data in fixed fields like in spreadsheets), but the vast majority of data in most organizations isn’t structured. Herein lies the significance of intelligent document processing: it transforms unstructured data into a structured format, ready for analysis and decision-making, helping businesses undergo complete digital transformations.

Intelligent document processing helps to considerably diminish manual intervention, making data processing more efficient, accurate, and speedy. It also assists organizations in making the shift to digital with ease and can reveal previously hidden insights and patterns that would otherwise be unnoticeable with traditional extraction methods.

On a larger scale, multiple industries are benefiting from intelligent document processing solutions. In banking and finance, documents like loan applications and customer account forms are processed more quickly and accurately, significantly reducing processing costs while increasing customer satisfaction through faster service. In healthcare, intelligent document processing is being used to digitize patient records, making healthcare more personalized, efficient, and safer.

With intelligent document processing, businesses can automate processes that include the ingestion of unstructured data. Intelligent document processing is a broad technology category that includes intelligent intake, which enables companies to automate the processing of virtually any kind of content, including emails, for processes such as insurance claims and underwriting, customer onboarding, and mortgage processing. Intelligent document processing automates the initial reading and categorization of documents as well as the extraction of key data, dramatically reducing document intake time while improving accuracy.

Related content: Unlocking efficiency: A guide to finding the best intelligent document processing software


Intelligent document processing solutions are built using tools like AWS and Microsoft IDP to read, interpret, and classify complex, unstructured information like invoices, contracts, emails, etc. These tools can often interface with, or even be written in, programming languages such as Python, which can include libraries and frameworks catering to intelligent document processing, making them more effective.

Stepping into the spotlight in the Intelligent document processing arena is Indico Data. Unlike other intelligent document processing companies that focus mainly on structured documents, Indico Data specializes in reading and organizing unstructured data. This, in turn, has been key in setting the tone for the company’s innovative and distinctive services within the intelligent document processing market.

Related content: Transformative shift: The power of Intelligent Document Processing for healthcare


In conclusion, the value proposition of intelligent document processing is astronomical, and it’s apparent that companies like Indico Data have strategically positioned themselves to ride this wave of digitization. The industry has seen great growth in recent years, and in many ways, this technology is still in its early days. It would be no exaggeration to say that the future of data management and digital processing is likely going to be formed by intelligent document processing, embracing unstructured and structured data together.

Frequently asked questions

  • What are the specific challenges or drawbacks associated with implementing Intelligent Document Processing (IDP)? Implementing Intelligent Document Processing (IDP) comes with a set of challenges that organizations need to navigate. One major issue is the integration of IDP systems into existing IT infrastructures, which can be complex and time-consuming, particularly in organizations with legacy systems. The initial cost of IDP solutions can also be high, involving expenses for both the technology and the training required for staff to effectively use these new tools. Additionally, the reliance on machine learning and artificial intelligence means that the effectiveness of IDP can be limited by the quality and quantity of the data available for training the algorithms. If the input data is biased or insufficient, the IDP system may produce inaccurate results or require extensive retraining.
  • How does IDP maintain data security and privacy, especially when handling sensitive information? Intelligent Document Processing systems are designed with various security features to handle sensitive information securely. Data encryption is commonly used to protect data both at rest and in transit, ensuring that unauthorized parties cannot access the information. Furthermore, IDP systems often include access control mechanisms that restrict who can view or edit sensitive data. Compliance with international data protection regulations, such as GDPR in Europe or HIPAA in the United States, is also a critical component of IDP solutions. These regulations enforce strict guidelines on data handling practices, and non-compliance can result in significant penalties. Regular audits and updates to these systems help maintain their security integrity over time.
  • Can you provide quantitative data or statistics to illustrate the improvements brought by IDP in areas like customer satisfaction or cost reduction? Several studies and reports have quantified the benefits of implementing Intelligent Document Processing. For instance, research has shown that IDP can reduce document processing times by up to 70%, significantly cutting down the operational costs associated with manual data entry. In terms of accuracy, IDP systems can achieve error rates that are substantially lower than manual processes, typically less than 1% compared to human error rates of 3% or more. In customer-facing operations, such as loan processing in banks or claim handling in insurance companies, the speed and accuracy of IDP have been shown to improve customer satisfaction rates by enhancing the speed of service delivery and reducing the incidence of errors in customer accounts. These improvements not only boost customer satisfaction but also enhance the overall efficiency and competitiveness of businesses adopting IDP technologies.

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