What is Intelligent Document Processing?
Intelligent document processing uses AI powered automation and Machine Learning (ML) to classify, extract, transform and enrich documents for consumption.
IDP is a combination of OCR with NLP and ML.
In the world without AI all this is done manually. And this would require a big team to go through the list of documents and manually identify the category the documents belong to and tag them with the right key words to make them searchable. This can take quite a long time if the volume of data is massive.Its time consuming, error prone and very costly.
But are we able to get insights from these documents?
Before automation digitization and digitalization has to take place.
‘Digitization is the conversion of physical documents with information into digital information.’
‘Digitalization is the utilization of this digital documents to derive context and transform the process.’
‘Data is a collection of numerical or non numerical facts.’
‘Information is data that has been processed, organized and given context making it meaningful.’
Key Takeaways
- Intelligent Document Processing transforms unstructured data into Business insights.
- Historical Data across industries holds untapped potential.
- Effective IDP starts with digitization and digitalization.
- The Intelligent Document Process follows a structured multi-phase workflow.
- AI techniques such as Named Entity Recognition and Object detection adds deep context.
How can intelligent document processing unlock hidden value in the different sectors?
In Financial Industry
In the financial industry that is banks, insurance and investment companies there is a retention period for financial documents.These documents are basically stored to meet the compliance and audit regulations. These archives often sit untouched for all that period before being disposed of after their retention period. These could be in digital storage if they have gone digital.
What if this historical financial data could be processed and analyzed through Intelligent document processing, a number of things could be uncovered. Things like uncovering of spending patterns, improving credit scores and personalizing financial products.
Without the Intelligent document processing the potential insight is locked away because of no efficient, cost effective way to process all the accumulated data both structured and unstructured.
The question is can we use this data to drive financial decisions?
In Legal Industry
In the Legal industry, law firms, courts and legal departments hold decades of contracts, case files and compliance documents. Most of the data is unstructured, scanned PDFs, handwritten notes and old case files which are usually stored for legal and regulatory requirements.
Suppose intelligent document processing is brought into the picture. In that case, this information can be classified, indexed and analyzed to reveal trends in litigation, identify contractual risks and can even be used to automate precedent research.
Valuable legal intelligence remains buried without an efficient document processing mechanism.
In Education Industry
In educational institutions such as schools, colleges and universities, huge amounts of students’ and institutions’ data are stored. From admission records, academic transcripts, research papers and administrative files. These are stored to meet accreditation and regulatory requirements. These are scattered across departments or archived on hard drives.
If this data is processed and analyzed using intelligent document processing, a lot of insights would be uncovered, such as students’ performance trends, maximize resource allocation and predicting dropout risks and learning gaps. All this valuable data lies unused without an efficient and cost-effective mechanism for document processing.
In Healthcare
Healthcare providers store medical records for several years as per regulations By analyzing a patient’s historical data, we can build a predictive model for any chronic disease.
Because of the lack of an efficient, cost-effective mechanism for document processing, it sits there unused.
Can we use this data to derive insights for better healthcare outcomes?
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Phases in Intelligent Document Processing (IDP)
After documents transformation by digitization and digitilization, the information is then utilized. This is where intelligent document processing (IDP) comes in. There are different Phases in Intelligent document processing, and these include Document capture, Classification, Extraction, Transformation, Enrichment, Validation and then Consumption.
Document Capture
In this stage all information is captured and placed in a secure, centralized and secure repository. Paper documents are scanned and converted into digital documents and the already digital documents are collected together for storage
This information comes from different document sources such as scanned documents, emails attachments, filled forms. And they can be in different document formats which can be structured or unstructured or even mixed both.And the document can be a single page or multiple pages. There are also different types of digital documents, image based JPEGs and PNGs or text based PDFs.
‘Structured data is data arranged in a systematic order such as tables, spreadsheets or databases. The information involved follows a specific pattern’
‘Unstructured data comes in the form of texts common in legal and contractual documents.’
In the central repository the documents need to be stored in adherence to the security, governance and compliance requirements. This ensures instant access from anywhere, anytime. The store needs to be scalable to ensure adjustment with the needs.
Document Classification
In this phase, the team previews the documents and then categorizes them into different specified folders. This can help later with information and metadata extraction.
The documents have to be classified properly to ensure the Intelligent document processing is flawless.
Document Extraction
This is the process of accurately extracting all elements which also includes structural elements. It helps identify key information from documents. The intelligent capture of data elements from documents during this phase helps in getting new insights in an automated manner. Example in the extraction stage is the Named Entity Recognition
For example James Kamau visited Oasis Hospital on 10th March 2025.Named Entity Recognition will automatically extract
Person: James Kamau
Organization: Oasis Hospital
Date : 10th March 2025
Named Entity Recognition (NER) – This is an AI technique that identifies and classifies key information such as names, dates, location, organization or monetary values in unstructured data.
Document Enrichment
Understanding the dynamic topics and document attributes is essential so as to get insights and business value from them.
For example a doctor’s note to verify medical condition mentioned in a claim form. Additional documents such as doctor’s notes would be required to derive medical information with details on medication and medical condition. This would enable business value such as improving patient care. What is required here is medical context, metadata, attributes and domain specific knowledge.
While named entity recognition will extract metadata from text of various document types, a process is required to recognize the non text elements in the documents. The object detection feature can be handy at this point.Personal information can be identified through Personally Identifiable Information (PII) detection methods.
‘Personally Identifiable Information (PII) is any information connected to a specific individual that can be used to uncover or steal that individual’s identity such as their PIN, Name, Email address or Phone number.’
Document Validation (reviewing and verification)
This is a phase where we check for the completeness of the claim form, also known as post-processing. A human workforce for manual review may be included in this phase for higher accuracy.
Only limited data can be processed as per business needs and requirements to minimize costs, as the human review can be quite costly.
Conclusion
To conclude, we have come to an understanding that Intelligent Document Processing is essential in different industries now that AI has become an integral part of our everyday lives. It has also changed the way we work.
How can we maximize this hidden potential to explore growth using historical insights?