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June 24, 2022
Intelligent Document Processing(IDP) is a solution that converts messy, unstructured and semi-structured data into a usable, structured format. The initial data set can be on offline documents like scanned forms and bills, or online documents like emails, PDF documents or images.
Intelligent Document Processing(IDP) is a solution that converts messy, unstructured and semi-structured data into a usable, structured format. The initial data set can be on offline documents like scanned forms and bills, or online documents like emails, PDF documents or images.
In simpler words, IDP solutions extract data from documents (PDFs, scans, emails, etc.) and convert them into usable text-based digitized data that is ready for processing.
Consider a bank, for example, that receives hundreds of different documents a day, from multiple sources like fax, post, and email, and in multiple formats like PDFs, forms, and images. These documents have to be categorized based on type (new account applications, insurance claims, credit card applications, identity proofs, etc.) and the data has to be manually extracted, processed, filled, and sent to the next step in the workflow. Current manual data entry processes are laborious, time-consuming, and highly error-prone. IDP integrates right from the start of the data lifecycle to the end taking over all manual operations. A good IDP solution should be capable of automatically categorizing documents, extracting, validating, and grouping data, and automating its flow to the right workstream.
According to an article by Automation Anywhere, 80% of business data is 'dark' - it is trapped within emails, documents, images, and hard copies, making it difficult to extract meaningful, actionable insights that can help businesses understand their customers and drive informed, data driven decisions.
The fact that data is critical for business decisions is not news, and businesses need a way of extracting and optimally using all their 'dark' data. This is exactly what IDP solutions do - classify, categorize, extract, and validate unstructured and semi-structured data to provide usable digital information.
The most important aspect - an IDP solution performs these operations without the need for any manual intervention. This becomes a pivotal feature when data from offline sources, like an insurance claim form filled in by hand, has to be extracted and documented. Normally, it would require a human resource to manually convert this data into online data and prepare it for processing. IDP solutions use technology like Natural Language Processing (NLP), Deep Learning, Machine Learning (ML), Optical Character Recognition (OCR), and Computer Vision to automate the process of data extraction from offline documents.
Data extraction is just one step in the process. Having explained what IDP does, let us understand the processes involved in each stage of data manipulation.
Intelligent Document Processing follows a set of steps to convert unstructured and semi-structured data into structured information. Each of these steps involves different technologies - machine learning, natural language processing, computer vision, RegEx, RPA, etc. So IDP is not a singular technology per se, but a solution that comprises individual technologies
While these steps could vary depending on the solution and provider, they generally are -
The first step in intelligent data processing is setting up different capture points. Data might be processed via hardware like scanners or might be present digitally as PDFs. In both cases, the IDP solution must be integrated and ready to capture data from documents, files or e-mails. It achieves this in two ways:
The IDP solution integrates with document processing hardware like scanners and uses tech like OCR to scan and extract data from paper documents.
It is also capable of ingesting online documents like word, PFD or excel files and extracting data.
At every point in the data capture process, the IDP solution pre-processes the document to improve its quality. This ensures that data extracted through OCR is accurate.
This step involves processes like Binarization (transforming data features into vectors of binary numbers), Noise Reduction (eliminating scribbles, marks), De-skewing (straightening scanned images), etc.
Documents being processed can be of multiple formats. Classifying, or grouping these documents helps improve the extraction and archiving processes.
By classifying data, the IDP solution can ensure that the right extraction technology is being used for the particular document format, and also that the data is being routed to the right workflow (depending on the nature of the document).
IDP uses trained ML and AI-based classification engines to automatically classify documents into different categories by analyzing their content and structure.
This step uses trained AI algorithms to extract data from documents. Computer vision technology like OCR is capable of extracting text from scanned documents and images, and NLP can identify hand-written data for accuracy.
This step not only extracts data but also defines the type of data being extracted - numbers, dates, names, etc. The level of accuracy depends on the level of training the AI algorithm has gone through.
Once data is extracted, it goes through a series of post-processing steps to validate its quality and accuracy. Data is validated through pre-defined logical algorithms and external databases. RPA is used to further enhance the level of validation and automatically pass data into relevant workstreams (claims data extracted from a user form can be automatically filled into a claims document and assigned to the claims department).
Any data that is flagged as inaccurate during the validation stage can be reviewed by a human, and all extracted data can also be verified by them to check for discrepancies. This process helps the AI algorithm learn and improve for future iterations.
Data capture technology, like OCR, has been prevalent for quite some time. Coupled with RPA bots, OCR can greatly reduce manpower and time by eliminating the need for manual conversion of data in offline sources into digital format.
Although often confused to be the same, OCR and IDP are different in what they achieve. OCR is a data capture technology - technology that extracts data from documents and presents it in textual form, and that’s it.
IDP, as is evident by the flow seen in the previous section, is more than just data capture. It also handles pre and post data extraction processes that make it a complete automation solution. In fact, OCR is a part of the entire IDP process. IDP solutions improve the quality of documents before extracting data and also validate extracted data to improve the accuracy of the result.
IDP solutions are, in layman's terms, an evolutionary form of data capture solutions.
The following use cases give a sense of how efficient IDP solutions are.
IDP for automation of cash management logistics - For banks, replenishing cash in ATMs is a resource intensive job. Traditionally, a field agent visits the ATM, clicks photos of slips with data logs, and another agent at the back office manually enters and processes this data. This manual data processing operation occurs for every ATM owned by the bank. With IDP, the task of processing data from these slips is completely automated. An IDP solution scans these slips, reduces noise, extracts data, validates it, and exports it to businesses processes that need this data. The IDP solution promises over 90% accuracy of extracted data and a manpower reduction of over 70%.
A U.S. Government body spent 5 years to integrate data from an archived data source using traditional data capture only to achieve subpar results. Using IDP, they were later able to integrate data from more than 50 million pages of records in under two years.
(Alternate for 4) An invoicing subsidiary of a Japanese multinational conglomerate implemented IDP and RPA to automate invoicing processes. The company processes over 80,000 invoices every year received from more than 1,000 vendors and in 20 different languages. IDP was set up to improve their procure-to-pay process, and they experienced a 100% reduction in errors ten months post-implementation.
The most obvious benefit of implementing IDP is the reduction in execution time. Traditionally businesses require a resource to manually review documents, extract data, and then process it. With automation, the entire process is completed in seconds with multiple documents being concurrently processed.
Businesses experience a reduction in operational costs because of the speed at which data is processed and made available for consumption. The manual intervention needed is little to none which means teams can work with a lean workforce, which is again a drop in operational costs.
IDP eliminates the need for manual work, completely eliminating human-prone errors. For businesses this results in increased quality and reliability of processed data.
IDP is not process or department specific. The solution can be extended across all departments and workflows that involve data extraction and processing. As an integrated solution, any team within the organization can process documents using IDP making it easily scalable.
A business looking to implement IDP does not need to procure new hardware or software. IDP solutions integrate with existing devices like printers and scanners, applications, and process all formats of documents. The ability to integrate with the existing framework contribute in making the implementation process effortless.
It is quite evident that businesses that rely on data can greatly improve processes by implementing an IDP solution. More specifically, businesses that deal with data that flows in from multiple channels and need to process this data for subsequent workflows to begin can greatly reduce time, errors and effort through IDP. Businesses like banking and finance, healthcare, legal, and accounting are examples.
The best way to identify if IDP is a viable solution is to test it. Demo AmyGB's IDP solution for free and run use cases to analyze its impact on day-to-day operations.