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Intelligent Document Processing & Its Top 30 Use Cases in 2024

Document automation is the practice of leveraging automation tools such as RPA to collect, extract data, and generate documents. Document automation has numerous benefits and use cases across different industries. However, it’s been estimated that 80-90% of enterprise data and documents are unstructured and require the integration of different technologies to turn it into a machine-readable format.

In this article, we explore how intelligent document processing (IDP) tackles the issue of unstructured data processing, how it works, and its top use cases.

What is intelligent document processing?

Intelligent document processing (IDP) is an emerging technology solution that enables businesses to automate document processes even when it involves unstructured data such as PDFs and images. Intelligent document processing is also known as IDP RPA because most IDP solutions leverage RPA to automate documentation processes.

How does IDP RPA work?

IDP RPA solutions leverage different technologies including:

  • OCR: to extract data from unstructured documents and convert it into machine readable formats.
  • NLP: to understand the language and meaning of the extracted data.
  • Machine learning algorithms: to classify and validate the data.
  • RPA: to automate data processing and manipulation processes (e.g. entering the data into excel sheets, generating reports from the extracted data).

What is the difference between OCR and IDP?

OCR is a technology that scans documents (PDFs, images, handwritten documents) and converts the text into machine-readable data. However, OCR faces many challenges such as skewed images, blurred text, or color variations. This is why it is no longer used on its own with businesses and is usually paired with different technology solutions such as image recognition or AI.

On the other hand, IDP is a solution that leverages AI (e.g. ML, deep learning, and NLP) to reduce the noise in the documents, understand the context of the text, and learn from incoming data.

IDP use cases in finance and banking

Since IDP combines the features of AI, RPA, and OCR, it is a great candidate for automating numerous tasks across the finance department, such as:

  1. Customer onboarding
  2. Invoice automation
  3. Credit scoring
  4. Financial reporting (daily, monthly, annual)
  5. Reconciliation automation
  6. Journal automation
  7. Customer risk profiling
  8. Cheque image processing
  9. Fraud detection
  10. Account opening and closing
  11. Refund processing
  12. Compliance automation

See our comprehensive article on RPA use cases in finance for more details on how the RPA component of IDP automates these tasks.

IDP use cases in insurance

  1. Claims processing
  2. Claims validation (e.g. classifying insurance coverage based on provided documents)
  3. Policy updates and notification automation
  4. Contract review
  5. AML check

See our article on RPA in insurance for a comprehensive overview of how RPA works to automate insurance processes.

IDP use cases in healthcare

  1. Electronic Healthcare Record (EHR) documentation management
  2. Patient onboarding automation
  3. Medical bill automation
  4. Medical recruiting and credential-checking
  5. Clinical trial data management

See our article on RPA use cases in healthcare for more details.

IDP use cases in retail and e-commerce

  1. Shipment tracking and notification automation
  2. Inventory tracking
  3. Invoice and billing automation
  4. CRM automation

IDP use cases in government and public sector

  1. Public sentiment analysis via survey data extraction and analysis
  2. Application document verification and approval
  3. Credential issuing and notification via messages or emails (e.g. ID, driver’s license, passport)
  4. Covid-19 case tracking and recording

For more on how RPA can be used by governments and public sector departments, read our in-depth article.

For more on RPA

To explore how AI-enabled RPA works and where it can be implemented, read our in-depth article:

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And if you are planning on investing in an off-the-shelf RPA or document automation solution, scroll through our data-driven lists of:

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This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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