Converting manual data into electronic documents is an essential step in most companies’ digital transformation.
To successfully accomplish this requires thoughtful planning and the right document-processing solution.
Document processing converts manual forms and analog data into a digital format so that these documents can be integrated into day-to-day business processes. By using a document-processing system to extract data, a company can digitally replicate the document's original structure, layout, text and images.
Document processing is ideal for converting documents with identical formats. If the formats are unrecognizable or inconsistent, the process may need to redirect to human operators to complete the conversion.
In the following video, Jamil Spain gives a breakdown of document processing:
What is intelligent document processing (IDP)?
Advances in artificial intelligence (AI) have enabled companies to automate document processing even further. Intelligent document processing (IDP) uses AI-powered automation and machine learning to classify documents, extract information and validate data. It further automates and speeds up document processing through automation and structuring unstructured data.
IDP may also incorporate robotic process automation (RPA) and natural language processing (NLP) tools to make the transition from analog to digital faster and less error-prone. RPA, in particular, can automate hands-on, point-and-click operations so there is less required human interaction with the process.
How does document processing work?
Document processing can be done using computer vision algorithms, neural networks or even manual labor. Typically, the process of digitizing analog data into digital data follows these steps:
- Categorize and extract the layout and structure: Document-processing solutions are rules-driven. Programmers create these pre-defined extraction rules before the work can begin. This includes defining the category and format of the documents. Once that is defined, the team can extract the layout and structure.
- Extract the document information: There are several methods teams can use to automate text transcription. Optical character recognition (OCR) scans the document for typed text from manual documents and transforms it into data. Intelligent character recognition, a type of handwritten text recognition (HTR), can recognize standard text as well as various fonts and styles of handwriting.
- Detect and correct document errors: OCR technology can be error-prone, which means extracted data may need manual review. When a document format cannot be processed or errors are identified, it can be flagged for human review and fixed through manual entry.
- Store document and data: The final document is stored in a format that allows it to integrate with current applications.
If you’re using intelligent document processing, it enhances traditional document processing by doing the following:
- Processing data faster: The advanced automation is a faster and more accurate way of extracting relevant information from unstructured and analog data. This shortens workflows by eliminating manual processes and reducing errors.
- Processing unstructured documents: Unlike traditional document processing, IDP can transform structured, unstructured and semi-structured information and apply the data to business applications and workflows.
- Increasing data accuracy: Machine learning enhances document classification, information extraction and data validation to improve processing quality and reliability. Using low-code supervised training within the workflow aims to improve accuracy overtime without having to reprogram extraction rules.
- Enhancing security: IDP stores documents and personal information in a secure (digital) location. This is especially important in industries like healthcare and financial services with strict security regulations and compliance policies.
- Reducing cost: The manual aspects of traditional document processing make it time consuming, taking experts away from other work. Automation shortens processing time, which decreases operational costs and better utilizes staff.
Best practices and challenges
Whether your organization is digitizing healthcare records or looking to streamline invoice processing, it helps to do some prep work and follow best practices to avoid costly, time-consuming problems once you begin. This includes the following:
- Document categorization: Author and organize documents according to function, which clarifies relative information for concise data extraction.
- Data conversion: Convert unstructured and semi-structured data into structured data that provides usable information for automation enhancement.
- Consider integration and APIs: Once the data is converted to a digital format, how will it be used within the organization? Will it be compatible and easily accessible to all who need it? Discuss the business needs with stakeholders to ensure it is properly integrated within your organization.
- Consult the experts: Talk to the people who use the information you are digitizing to better understand its value to the business how it the information should be interpreted. This will ensure that whoever is addressing errors understands what the data should look like and that the process is done right.
Traditional document processing does come with some challenges that should be considered before a digital transformation project begins to avoid delays:
- Only uses one format for processing: Document processing uses pre-defined extraction rules to transform the relevant information into digital form. This type of data capture works great for structured data where the information is consistent. However, if you have large volumes of unstructured data or complex documents where the information provided is not consistent, the process can result in time-consuming errors.
- Relies on processing experts: When issues and errors arise, they are often flagged for manual review by processing experts. This can be time consuming and require significant human resources.
- Difficult to continuously improve: Document processing systems lack operational visibility into how your document processing is functioning and what errors are commonly slowing the process down.
Use cases for document processing
These are a few of the most common situations in which you could use document processing:
- Invoice/payroll: Digital transformations require manual invoicing and payroll systems be digitized and automated. Using a tool like IBM's Automation Document Processing, you can configure and use a pre-defined deep learning model for data extraction for the invoicing process.
- Insurance: Document processing allows you to extract data from forms and quickly verify coverage and eligibility. It also keeps documents consistent with industry standards and protocols and protects sensitive documentation and personal information.
- Human resources: Use document processing to convert employee and candidate data into valuable insights that optimize staff management and hiring decisions.
- Fraud detection: Document processing has become a valuable tool to financial services, authorizing signatures on checks and determining the authenticity of high-volume transactions to eliminate banking discrepancies.
- Mortgage: Mortgage processing requires that lenders process millions of paper documents each year. Document processing ensures quick and simple document retrieval and increases the speed and scale of mortgage filing.
Document processing and IBM
IBM Cloud Pak® for Business Automation, IBM’s leading offering for document processing, takes your automation a step further by infusing artificial intelligence (AI). Its features are designed to improve both your internal processes and your customers' experiences.
To get more insights into document processing, automation and the latest in AI, subscribe to the IBM Business Automation Insider. Learn how the latest products work, implement best practices and maximize your tech investments.