Introduction
What is document processing?
Document processing automates the conversion of unstructured content buried in business documents into structured data that is useful for business processes. It enables business users to extract valuable data more easily and accurately from paper documents, electronic documents and images.
How it’s used
Extract the information you need
Read and extract the data you need from structured, semi-structured and unstructured business documents – regardless of format. Classify documents by type to determine the data fields to extract. Machine learning models extract data from forms and statements while natural language processing extracts data from conversational text. Document processing also determines whether a document has been signed or a box has been checked.
Eliminate errors to produce trusted data
Automatically detect and correct errors in extracted data or document classification that could create bottlenecks. AI-powered services produce data you can trust to drive better business outcomes. Use human-in-the-loop capabilities to flag issues, add missing data and verify your extracted data. Standardize what your data looks like by automatically formatting or converting text from source documents.
Get your data where it needs to go
Apply extracted data from documents to your downstream applications and business processes. Feed data to workflows, RPA bots or business applications. Archive or declare documents as records for long-term retention to comply with regulatory requirements. Set security permissions or redact sensitive information based on user roles. Use data to uncover patterns and insights to drive more informed business decisions.
What you get
Document processing features
No-code setup experience
Create a document processing flow with a visual, click-through approach to building applications.
Data extraction
Extract data from structured, semi-structured and unstructured documents.
Classification and categorization
Identify disparate documents and sort them into the appropriate buckets.
Automatic error correction
Detect and correct data that’s been extracted incorrectly or should be enriched.
AI at every stage
Infuse AI throughout a business process, from data collection and enrichment to the training of new document types.
Out-of-the-box templates
Adapt to any business need with prebuilt templates that allow you to tailor a process relevant to your documents.
Easy-to-train models
No data scientist is required to set up an application or train a machine learning model.
Automation foundation
Shared components
Build once and reuse
A set of common AI and automation components power each IBM Cloud Pak and provide security-rich integrations between them — so you can build once and then reuse across your business and IT operations. Key components include:
- Process mining to identify trends, patterns and details of how a process unfolds
- Task mining to find low-hanging RPA opportunities
- Robotic process automation to automate repetitive tasks
- Unified asset repository to store and share reusable automation artifacts
- Single event hub to process event data in real time and feed machine learning.
Work orchestration
Personal, interactive AI
Give workers their own interactive AI — in tools they already use, like email, calendars and Slack® collaboration software — to help them perform routine and mission-critical tasks faster. Initiate work just by speaking and then a powerful AI engine goes to work combining prepackaged skills based on organizational knowledge and prior interactions.
Enterprise-grade containers
Deploy anywhere
The automation foundation and IBM Cloud Paks are containerized software that run on Red Hat® OpenShift®, an enterprise-ready Kubernetes platform. Such containers are ready to deploy anywhere: hybrid cloud, multicloud and edge. Red Hat Open Shift offers one point of control to simplify orchestration across all of your environments.
IBM certifies and manages the container templates to automate the software lifecycle from configuration to monitoring, scaling, compliance and patching. Security hardening techniques reduce the chance of even common vulnerabilities.
Key use cases
Account servicing
Jump-start the document training process by reading common fields like “customer address” and “account number” or directly reading PDF form definitions. Use RPA to automate the entry of data into back-end systems. Identify account closing requests and forward to agents to increase customer retention.
Automated enrollment
Eliminate inefficient, manual spreadsheet processing for program enrollment. Guide users through a step-by-step process, training the system to recognize key fields from enrollment forms. Ensure that data and currency fields are accurately recognized and use custom validators to handle unique fields.
Faster customer quotes
Accelerate the customer quote and approval process by automatically reading and classifying application documents and extracting the appropriate data. Extracted data is returned in a standardized format and, with human-in-the-loop validation, is complete and accurate so you can respond with a quote faster than your competitors.
Everest Group named IBM a leader among intelligent document processing (IDP) technology vendors.
