Intelligent Document Processing market grows as important subset of digital transformation

As the pace of digital transformation accelerates, the Intelligent Document Processing (IDP) market is ramping up. Here’s a snapshot of IDP―what it is and why it matters.

A recent Markets and Markets study expects the global Intelligent Document Processing (IDP) market to grow at a compound annual gross increased rate (CAGR) of 37.5% from 2022 to 2027. This growth is being driven by the pressing need for organizations to process large volumes of semi-structured and unstructured documents, store them and access them securely, and make them available for advanced analytics. Today, this workflow is part of a company’s operational heart. How enterprises handle document processing using IDP solutions is a showcase for how this technology category is an important enabler of faster operations, innovation, and competitiveness.

What is Intelligent Document Processing?

IDP is the use of artificial intelligence (AI) and machine learning (ML) technologies to automate the processing of unstructured data contained in documents. IDP systems are designed to extract, classify, enrich, structure and search relevant information from documents like invoices, forms, contracts, orders, receipts, and customer and employee emails.

Traditional document processing often involves manual data entry and extraction, which is time-consuming, error-prone, and inefficient. IDP automates these tasks by using AI algorithms to analyze and interpret the content of documents. 

The results can be powerful.

Banks and insurance companies can automate the processing of loan applications, mortgage documents, and insurance claims, reduce manual errors, accelerate decision-making, and improve customer satisfaction. Healthcare organizations can streamline the processing of patient records, insurance claims, and medical invoices. IDP systems can extract key patient information, automate billing processes, and ensure compliance with regulatory requirements. Law firms can automate their paper stream and IDP systems can be used to extract relevant clauses, terms, and other key data.

Tackling Complexity

The IDP process begins with the acquisition of documents. These documents can be in various formats, such as PDFs, images, emails, or scanned copies. Data can be collected from multiple sources, including customer portals, email attachments, or file repositories. Advanced data acquisition techniques, like Optical Character Recognition (OCR), are used to convert the documents into machinereadable formats.

After digitizing the documents, the next step is to classify them based on their content and purpose. IDP systems employ machine learning (ML) algorithms and natural language processing techniques to automatically categorize documents into predefined classes. Classification enables efficient routing of documents to the appropriate workflows or departments for further processing as needed.

Once documents are classified, the IDP system extracts relevant information. This process involves identifying key fields, such as names, addresses, dates, invoice numbers, and financial data. IDP leverages various techniques, including pattern matching, text recognition, and ML to extract data accurately. These systems continuously learn and improve their extraction capabilities over time, adapting to new document formats and layouts.

To ensure data accuracy, the extracted information undergoes validation and verification. IDP systems compare the extracted data against predefined rules, databases, or reference documents to validate its correctness. For example, an invoice amount can be validated by cross-checking it with the corresponding purchase order or contract. Any discrepancies or errors are flagged for manual review and resolution.

The extracted and validated data is then integrated into existing business systems or workflows. IDP platforms offer integration capabilities with enterprise resource planning (ERP) systems, customer relationship management (CRM) software, or other relevant applications. This seamless integration eliminates manual data entry, reduces the risk of human errors, and enables efficient end-to-end process automation.

While IDP systems excel at automating document processing, there may be cases where documents require human intervention. Exception handling mechanisms are in place to identify and route documents that couldn’t be processed automatically or require review. Users can intervene to correct errors, resolve discrepancies, or provide additional information. These interactions further train the IDP system and improve its future performance.

IDP solutions often provide analytics and reporting functionalities to monitor and analyze document processing performance. Key performance indicators (KPIs), such as processing time, accuracy rates, and throughput can be measured and tracked. This data helps organizations identify bottlenecks, optimize workflows, and make data-driven decisions to enhance overall document management efficiency.

In a new report on the IDP Market and Iron Mountain’s role in the market, analysts at Moor Insights & Strategy stressed that companies shopping for an IDP solution provider should look for scalability, security, integration with third-party software, and the depth of experience of the vendor. 

Read the paper by Moor Insights & Strategy on Iron Mountain’s role in the digital transformation value chain.