Intelligent Document Processing Use Cases and Best Tools in 2025

Almost 80% of company data today is unstructured, including bills, contracts, receipts, forms, and emails (Gartner).  Without the right resources, this data becomes difficult to handle, slowing workflows and increasing the chance of errors.

This is where Intelligent Document Processing (IDP) comes in. Simply put, IDP is the use of AI, machine learning, OCR, and natural language processing to automatically extract, classify, and validate data from documents, whether they are structured like invoices or unstructured like contracts.

Organisations from a variety of industries are using IDP software solutions, such as KlearStack, to manage complex document workflows at scale as the volume of enterprise documents increases annually.  

These solutions guarantee compliance and audit readiness in addition to increasing speed and accuracy by automating manual activities.

This blog will examine the most common applications of intelligent document processing and demonstrate how companies may utilise IDP to increase productivity, cut expenses, and create more intelligent processes.

What is Intelligent Document Processing and Why is it Essential for Modern Businesses?

IDP uses artificial intelligence, machine learning, NLP in place of manual entry or simple OCR tools to collect data, comprehend its context, and confirm accuracy across a variety of document types. 

It works with both structured formats like invoices and unstructured inputs such as contracts, emails, or handwritten forms.

In the data-driven world of today, the importance of IDP is obvious. Traditional approaches are expensive and prone to mistakes.  

By guaranteeing quicker turnaround times, fewer mistakes, and workflows that are approved, IDP removes inefficiencies and scales well with company expansion.

IDP has emerged as a key factor in automation, compliance, and competitive advantage for businesses looking to automate and optimise their processes.

Industry Use Cases: Intelligent Document Processing Use Cases

1. Healthcare: Accelerating Claims and Billing

Healthcare providers deal with thousands of unstructured documents daily, from medical records to insurance claims. Manual handling leads to errors, delays, and compliance risks.

Omega Healthcare processes complicated medical billing and claims paperwork with document automation. 

The organisation achieved 99.5% accuracy, reduced processing time by half, and saved more than 15,000 employee hours per month with IDP.  

Better compliance and quicker reimbursements were made possible by this.

(Source: Business Insider)

2. Mortgage and Lending: Automating Loan Document Processing

Bank statements, loan applications, and closing documents are among the many documentation that mortgage businesses must deal with.  This procedure is laborious and prone to noncompliance.  

Indecomm extracts and categorises mortgage documents using Amazon Textract in its IDX platform.  With 97% extraction accuracy and 100% classification accuracy, documents that formerly required 30 minutes each may now be processed in 5–7 minutes.  

In a lending sector where competition is fierce, the automation resulted in significant cost savings and quicker loan approvals, bringing expenses down to just $0.02 per page.

(Source: PR Newswire)

3. Insurance: Faster and More Accurate Claims Handling

Rising claims numbers force insurance companies to make decisions more quickly and with a lower risk of fraud.  IDP for unstructured claims documents was implemented by Zoom Insurance.  

By reducing manual errors by 85% and enabling a 70% faster claims turnaround, the system directly increased operational efficiency.  In addition to saving money, quicker and more accurate claims also increase consumer trust, which is a crucial differentiator in the insurance sector.

4. Real Estate: Streamlining Deal Documentation

Contracts, property records, and compliance files, which can amount to hundreds of pages, are all part of the deal documentation in the real estate industry.  Deal cycles are slowed down by manual scrutiny.  

IDP was used by Cushman & Wakefield to optimise their workflows, which were heavily reliant on documents.  The business accelerated deal processing by 70% and saved 16,000 manual hours a year.  

They were able to keep ahead in a cutthroat market by quicker customer onboarding and timely transaction closing with IDP.

5. Finance: Automating Global Invoice Processing

Every year, hundreds of thousands of invoices in various languages and formats are handled by large businesses.  Payment delays and bottlenecks result from manual processing.  

A multinational corporation with headquarters in the United States automated more than 250,000 multilingual invoices annually by integrating IDP with Oracle ERP.  

This project achieved a 60% automation rate, saved over 4,000 hours annually, and decreased expenses.  Additionally, the system guaranteed adherence to international accounting standards.

6. Fintech: Expense and Compliance Automation

Fintech firms often manage huge volumes of receipts and expense reports that need quick validation. A Korean enterprise implemented an AI-powered IDP solution to automate expense processing. 

The company achieved an 80% faster turnaround, better compliance monitoring, and greater employee satisfaction by removing manual data entry tasks. For financial companies, this means reduced operational risks and improved audit readiness.

(Source: Cornell University) 

7. Retail: Optimizing Order Processing

Retailers oversee enormous order volumes, frequently spanning several formats and geographical areas.  Manual processing lengthens turnaround times and raises the possibility of inventory errors.  

IDP was implemented by Connox, a leading online furniture retailer, to handle order-related paperwork.  Faster fulfillment and more seamless customer experiences were made possible by the reduction in order fulfillment time from thirty minutes to just five minutes.  

Additionally, the enhancement prevented shipment delays and decreased operating expenses.

Leading Intelligent Document Processing Softwares

1. KlearStack

KlearStack is a cutting-edge, AI-first solution for intelligent document processing. It eliminates the need for rigid templates by utilizing self-learning AI models that can adapt to organized, semi-structured, and unstructured materials.

Accuracy: Consistently high accuracy via AI-powered contextual learning.

Scalability: Can handle millions of documents without extensive reconfiguration.

Integration:  APIs enable easy integration with ERP, CRM, and accounting systems.

Cost-effectiveness: A shorter setup time and minimum manual involvement lower TCO.

Best fit for enterprises seeking a flexible, scalable, and quick-to-deploy IDP solution.

Want AI-driven accuracy, ease of integration, and cost-efficiency?

Get in touch with KlearStack today!

2. UiPath Document Understanding

UiPath Document Understanding is a popular solution for firms who have previously invested in RPA workflows. It combines automation and data extraction.

Accuracy: Reliable, but requires initial training for various document kinds.

Scalability: Effective in RPA-heavy companies.

Integration: A robust ecosystem, particularly with UiPath bots.

Cost-effectiveness: Additional licensing may be required for advanced functionality.

3. ABBYY FlexiCapture

ABBYY FlexiCapture, known for its OCR leadership, is ideal for sectors that handle a high volume of forms, invoices, and structured documents.

Accuracy: Very high for structured data; inadequate for unstructured stuff.

Scalability: Enterprise-ready, but requires a significant setup.

Integration: Supports most business platforms, but requires configuration.

Cost-effectiveness: Premium pricing is appropriate for large organizations.

4. Hyperscience

Hyperscience provides a unique approach by integrating AI-driven document processing with human-in-the-loop learning, ensuring that models develop in real time based on user feedback.  

It is commonly used in industries such as insurance, government, and financial services, where accuracy and compliance are essential.

Accuracy: Good, but may require manual review in the early stages.

Scalability: Ideal for high-variance documents across industries.

Integration: Provides APIs, but fewer out-of-the-box integrations than others.

Cost-effectiveness:  Initially expensive, but returns on investment are higher in the long run.

5. Automation Anywhere IQ Bot

IQ Bot is a member of the Automation Anywhere RPA ecosystem, making it an  alternative for businesses seeking end-to-end automation of document operations.  

It utilizes cognitive skills to identify, extract, and evaluate data while effortlessly integrating into automation workflows.

Accuracy: Works nicely when combined with Automation Anywhere bots.

Scalability: Enterprise-ready, but limited to the Automation Anywhere environment.

Integration: Works seamlessly with the Automation Anywhere suite; restricted outside of it.
Cost-effectiveness: Organizations that do not use their full RPA platform may incur additional costs.

Conclusion

IDP systems automate data extraction, classification, and validation, reducing manual errors, processing costs, and decision-making time.  

The practical examples from banking and insurance to healthcare and logistics, demonstrates how organizations are already seeing measurable ROI.  

Businesses can take advantage of platforms such as KlearStack, which provide an AI-powered, scalable solution that adapts to industry-specific needs and assures compliance while increasing productivity at scale.

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