What is Intelligent Document Processing (IDP)?
What is Intelligent Document Processing?
Intelligent document processing (IDP) is a newer technology that automatically classifies, extracts, validates, and exports data from documents using artificial intelligence and machine learning. IDP platforms can ingest structured, semi-structured, and unstructured documents like PDFs, emails, scanned papers, and images. They then identify the document type and layout to quickly and accurately pull out relevant data without any human involvement.
IDP emerged in the 2010s as an evolution of earlier data capture techniques. It came about as AI and computer vision advanced enough to automate document processing at scale with high accuracy. Traditional data entry by humans is time-consuming, expensive, and prone to errors. IDP provides a more efficient way to digitize information from paper and electronic documents. The AI "reads" files, learns document layouts, locates fields, extracts data, and exports it. This removes repetitive manual data entry so people can focus on value-added tasks.
IDP mimics how people process documents by using advanced OCR, natural language processing, templates, validation rules, and machine learning algorithms. It can handle both structured forms and unstructured formats like invoices, statements, applications, surveys, certificates, tax files and more. The extracted information integrates seamlessly into databases, applications, and backends. Some key capabilities of IDP platforms include:
- Classifying document types
- Understanding layout and context
- Finding keyword patterns
- Locating fields and tables
- Extracting handwritten and printed text
- Capturing checkboxes, signatures, logos
- Validating data accuracy
- Exporting data to desired formats
IDP delivers efficiency, reduces costs, minimizes errors, and speeds up document processing for both paper-based and digital workflows. It's an intelligent automation technology that mimics human understanding to liberate people from repetitive data tasks.
Benefits of Intelligent Document Processing
Intelligent document processing (IDP) offers numerous benefits compared to manual data entry and traditional OCR software. The key advantages of IDP include:
IDP automates time-consuming data entry, allowing employees to focus on more value-added tasks. It extracts and organizes data from documents much faster than human data entry.
Companies can process documents in hours rather than days or weeks. This accelerates business processes that rely on information in documents.
Humans make mistakes when entering data, resulting in errors that affect downstream processes. IDP uses AI and machine learning to extract data more accurately than people.
IDP systems continuously improve, learning from exceptions and expanding knowledge. This results in higher data accuracy over time.
IDP automates the labor costs associated with manual data entry. This reduces staffing requirements and expense.
It minimizes the need for data verification and quality checking that add costs. The accuracy of data extracted by IDP systems results in fewer errors that require correction.
IDP systems scale easily to handle large volumes without adding staff. This allows organizations to grow without linearly increasing labor costs.
IDP improves compliance with regulations requiring data privacy, security, and retention. All processing is recorded and auditable.
Sensitive data can be automatically redacted. Biometric and other personal data can be found and removed.
IDP systems integrate with leading content management and records management systems. This aids compliance with data governance regulations.
Use Cases for Intelligent Document Processing
Intelligent document processing (IDP) has a wide variety of use cases across many different industries. Some of the most common and impactful use cases include:
Accounts Payable and Invoices
One of the biggest use cases for IDP is in accounts payable departments for invoice processing. IDP allows invoices received from suppliers to be automatically ingested, parsed, and entered into ERP systems like SAP without any manual data entry. This reduces invoice processing costs by over 50% and decreases approval cycles from weeks to days.
Key information like invoice numbers, supplier details, dates, and line item amounts can all be automatically extracted with high accuracy. This helps accelerate payments to suppliers and improves relationships.
Insurance firms and healthcare providers receive millions of claims every year. IDP helps rapidly classify, extract information, and process these claims automatically without human review.
This reduces processing costs, speeds up claims fulfillment, and improves customer satisfaction. IDP also helps reduce fraudulent or improper claims by flagging inconsistencies for manual review.
Organizations handle thousands of legal contracts with customers, vendors, partners etc. IDP helps automatically classify different contract types, extract key terms, obligations, dates and legal entities.
This allows organizations to better understand contract commitments, track deliverables, and monitor contract lifecycles without tedious manual reviews. It also aids in contract searching and discovery.
Banks, financial services firms, telecoms and utilities companies need to process multitudes of customer acquisition forms. IDP helps automatically ingest completed forms, extract customer data, validate information, and load into CRM/ERP systems.
This dramatically reduces manual data entry, speeds up customer onboarding, and provides a better customer experience. IDP helps companies scale customer acquisition without proportionate growth in overhead.
So in summary, IDP enables process automation for document-heavy tasks in accounts payable, claims management, contracts and customer onboarding. This drives significant cost savings, faster turnaround times, improved accuracy and better experiences for end customers. It allows companies to handle increasing volumes without linearly growing staff.
IDP vs Traditional Data Entry
Intelligent document processing has significant advantages over traditional manual data entry in terms of accuracy, speed, and cost.
With manual data entry, there is a high risk of human error leading to inaccurate data. Humans can misread handwritten or poorly scanned documents, enter data in the wrong fields, or make typos. This can cause major problems if incorrect data enters a company's systems. IDP leverages AI and advanced OCR technology to extract data more accurately than humans can. Some IDP solutions like LedgerBox achieve over 99% accuracy on scanned documents.
IDP is also much faster than humans at processing high volumes of documents. While an employee may only be able to manually enter a few hundred documents per day, IDP can process thousands of documents per hour. This increased throughput allows companies to extract data from their backlogs much faster.
Lastly, IDP dramatically reduces the labor costs associated with manual data entry and document processing. Hiring teams of employees to type forms and invoices is expensive and inefficient. IDP automates most of this repetitive work at a fraction of the human cost. And because it's more accurate, there is less need to spend money fixing data entry mistakes. The savings from adopting IDP are often substantial.
So in summary, IDP beats traditional manual processes in accuracy, speed, and cost - allowing companies to leverage documents better.
Intelligent document processing solutions are powered by a combination of AI technologies including optical character recognition (OCR), natural language processing (NLP), and machine learning. These components work together to automate the extraction of data from documents.
Optical character recognition (OCR) is used to convert images of text into machine-readable text. OCR engines use computer vision techniques to identify characters in images and convert them into standard text formats. This allows unstructured data in scanned documents, PDFs, and images to be extracted and processed by other components.
Natural language processing (NLP) analyzes text to understand its full meaning and context. NLP techniques like semantic analysis and contextual analysis help IDP solutions better interpret the information in documents for more accurate data extraction. This allows IDP systems to handle real-world documents with complex formatting and language.
Machine learning and artificial intelligence enable IDP platforms to continuously improve their accuracy over time. The system learns based on large volumes of training data to build intelligence. As the system processes more documents, the algorithms become better at handling variations in layouts, formats, and content. This allows IDP solutions to scale across diverse document types.
Leveraging the technologies above, IDP solutions are able to automatically identify and extract relevant data fields from documents without any human intervention. This includes structured data like names, addresses, dates as well as unstructured text and tables. The extracted data can then be exported into databases and business applications for further processing and analysis. Eliminating manual data entry accelerates business processes and reduces costs.
Intelligent document processing solutions typically consist of software components like OCR, NLP, machine learning, and rules-based engines. To implement IDP, these components need to be integrated into an organization's existing workflows and systems. This integration allows for automated extraction of data from documents so it can flow directly into business applications.
Several steps are involved in implementing IDP:
Assess current document and data workflows. Look at where paper or digital documents are coming into the organization and what kind of data needs to be extracted from them.
Determine integration requirements. IDP software will need APIs and connectors to integrate with existing content management, ERP, CRM, or other systems. Data output formatting needs should also be defined.
Create taxonomies and configure data extraction models. Taxonomies classify document types and data fields. Extraction models are built and trained to identify documents, fields, and values. This setup is key to accurate automated data capture.
Set up workflows and validate accuracy. Workflow steps link scanning, classification, extraction, validation, and export of data. Humans check a sample set of documents to validate extraction accuracy before full deployment.
Monitor and refine. Once in production, IDP workflows should be monitored. Errors should be fed back into the system to retrain extraction models and improve accuracy over time. Taxonomies may also need to be extended to handle new document types.
Update integrations as needed. As business systems change over time, IDP connections will need to be maintained and updated. IDP platforms provide APIs to build these integrations.
With the right software and implementation approach, IDP can deliver tangible benefits from automated document processing. The key is carefully configuring IDP to existing workflows and validating accuracy before full rollout. Maintaining the system improves results over time.
Challenges of IDP
Intelligent document processing solutions aim to automate the extraction of data from documents, but they face some key challenges around data quality, handling complex documents, and customization.
A major challenge with any automated data extraction is ensuring the output data is accurate and reliable. IDP systems rely on advanced AI and machine learning, but they can still struggle with data quality in some cases:
Documents with poor scan quality or unusual formats can cause errors in text extraction.
Handwritten text and complex tables/charts are difficult for algorithms to interpret accurately.
Extracting contextual data like dates, names, addresses remains an imperfect process prone to mistakes.
Ambiguous content, like abbreviations or industry jargon, increases misinterpretations.
Maintaining high accuracy requires extensive training data and often some human review.
Many documents like contracts, financial forms, medical records have complex formatting and layouts that pose difficulties for automated processing:
Documents with complex structures, tables, diagrams require more sophisticated algorithms.
Identifying the semantic meaning and relationships within documents adds another layer of complexity.
Multi-page documents make it harder to maintain context and data lineage across pages.
Low-quality scans and image-based PDFs are challenging to process with OCR and text extraction tools.
As a result, IDP success rates tend to be lower with more complex documents.
While IDP solutions provide pre-built extraction capabilities, many businesses need customization to handle industry or company-specific documents and data needs:
Templates and fields require configuration to match proprietary document types.
Unique business rules and validation logic may need to be incorporated.
Integration with surrounding databases, workflows and other systems is often essential.
Ongoing maintenance is necessary as new document types emerge.
The more customization required, the more costs and implementation work is involved.
Intelligent document processing is an emerging technology that is rapidly evolving. Some key trends shaping the development and adoption of IDP include:
Many IDP solutions are moving to the cloud. Cloud-based IDP can offer benefits like lower start-up costs, greater scalability, faster deployment times, and automatic updates. As more organizations embrace cloud technology, demand is growing for IDP systems that can be accessed as cloud services.
There is increasing emphasis on enabling mobile capture and extraction of data from documents. Many IDP vendors now provide apps to snap photos of documents or scan documents using a mobile device's camera. These apps can instantly extract key data fields and route documents into workflows. Mobile IDP allows for greater flexibility and accessibility.
Advanced AI Capabilities
Artificial intelligence is being incorporated into IDP systems to enable more automated classification, information extraction, and data validation. With machine learning, IDP tools can continuously improve their accuracy over time. AI also powers advanced capabilities like sentiment analysis, predictive analytics, and personalized workflows based on AI-driven insights. As IDP leverages more robust AI, it delivers higher automation rates.
Leading IDP Solutions
Intelligent document processing is a rapidly evolving technology, and several leading software vendors offer mature IDP solutions:
LedgerBox offers a comprehensive IDP platform that includes optical character recognition, intelligent document classification, data extraction, validation and integration.
IBM offers Datacap as part of its wider intelligent document processing capabilities. Datacap provides capture, classification, extraction and validation using cognitive technologies like OCR, NLP and machine learning. It can handle both structured and unstructured data from documents. Datacap integrates with other IBM offerings like DataPower, StoredIQ and Content Manager.
Kofax provides a complete range of capture, process automation and analytics solutions for IDP. Its offerings include Kofax Capture for document capture, Kofax Transformation Modules for data extraction and validation, and Kofax Analytics for process visibility. Kofax leverages cognitive technologies like machine learning to drive automation.
Other leading IDP solution vendors include WorkFusion, Hyland, Parascript, Hyperscience, Rossum, and Infrrd. These providers offer IDP platforms with capabilities like document classification, information extraction, and data validation. The IDP market continues to grow as more organizations adopt these intelligent technologies.
The Future of IDP
Intelligent document processing is still in the relatively early stages of adoption, but is expected to see rapid growth and evolution in the years ahead. Here are some key trends we expect to shape the future of IDP:
As more organizations realize the benefits and cost savings of automating document-based processes, IDP adoption will accelerate. While IDP started in a few key industries like financial services and insurance, it is now spreading into new sectors including healthcare, legal, government, and more. Strong demand will also drive more IT services firms to build IDP expertise.
Today most IDP deployments are focused on individual processes or departments. As companies gain experience with IDP, we'll see broader integration at an enterprise level. IDP platforms will increasingly integrate with core business systems like ERPs, CRMs, and content management to connect data flows across the organization.
While IDP emerged for semi-structured documents like forms and invoices, new AI capabilities are enabling applications for unstructured or complex document types. For example, IDP is now being applied to contracts, claims processing, patient charts and more. Ongoing AI advances will unlock IDP for new document types and business challenges.
In summary, intelligent document processing is poised for greater growth and sophistication. As IDP platforms improve and integrate more deeply into business processes, they will become an indispensable tool for digital transformation across many industries. Companies not leveraging IDP risk falling behind competitors who use it to boost productivity and leverage information within documents.