Pacing ahead to conquer the next-gen technology & drive business growth
Talking about technological advancements and how it has simplified our day to day activities, automation has impacted not just personal but professional lives in myriad ways.
Robotic Process Automation (RPA), a technology to automate routine, and repetitive business processes has witnessed dramatic surge in adoption globally, across industries. Yet, there remains a gap in fulfilling the automation goals of every organization due to the inability to automate certain tasks related to extracting data from multiple types and formats of documents used in different industries today.
Business processes that are extremely common in the BFSI industry, like Invoice Processing, Insurance Claim Processing, Know-your-Customer (KYC) etc. are top of the trend use cases for which companies have adopted RPA.
However, none of these processes are 100% automated due to the restrictions of the technology to extract data from multi-format (Scanned Images, Pdfs) documents like:
Invoices or Purchase Orders
PAN, Adhaar, Passport
Account Opening Application Forms
Bank Statements, Cheques, etc.
RPA tools have been leveraging the capabilities of Optical Character Recognition (OCR) to extract data from such documents but have not been able to fill the gap completely.
OCR is based on a technology called “Computer Vision” which is used to comprehend images and convert them into characters and symbols that humans and machine can understand. From the conceptual definition it looks like OCRs are the ultimate resolution to data extraction challenges, yet there are miles to cover.
OCRs yield good results for certain specific use cases only, but in order to cater to enterprise-wide use cases, we need to move toward solutions that are dynamic in nature to be able to adapt to the changing customer requirements, thereby acting intelligently, the way humans do.
OCRs operate on a preloaded library of multiple variants of characters, symbols, alphabets etc. While converting an image into text, OCR matches the patterns with the data in its library, and if it gets a match it is able to successfully extract the data. This restricts the ability of the OCR to learn and enhance its capabilities which is imperative to deal with enterprise wide use cases of data extraction.
We need a solution that operates on the concept of Machine Learning (ML) and is able to learn dynamically and throw results with improved accuracy* and enhanced capture rate*.
*Capture Rate = Amount of data extracted from a document
*Accuracy = Accuracy of the captured content
Cognitive Machine Reading, a smarter and intelligent way of understanding and extracting data from several documents could only be enabled by AI-powered data extraction solutions. We need a solution that is not merely converting an image into a text, but is able to:
Perform digitization of unstructured data - Convert all elements of the document to Machine Readable form
Images, handwritten text, printed text etc.
Comprehend and differentiate between multiple elements of a document
E.g. Image / Table / Signature etc.
Enhance the readability of the document
E.g. Remove stains, ink marks, dark regions, folds, shadow etc.
Apply business rules to extract the required data
E.g. Extract photo and signature into two separate files
Perform quality check on the data extracted and provide confidence score to enable supervised learning and improvement in the accuracy by indicating data that is:
Accurate
Not Certain
Not Captured
OCRs have a life as they are unable to cope up with the dynamic business environment and increasing diversity in the type and formats of the documents. You need a solution that is not just suitable for your current requirements but is also able to adapt to your future needs, else you will end up looking for another better OCR solution for your evolving business processes.
Technology landscape has shifted from specific targeted solutions to more dynamic and adaptable solutions so that you are able to overcome current challenges and position yourself for future ones.
| Parameters | Traditional OCRs | AI based Cognitive Machine Reading (CMR) |
|---|---|---|
| Self-Learning | OCR functions on a predefined library | CMR is able to learn and evolve |
| Applicability | Suitable for few uses cases - Dependent on templates and Zones | Suitable for wide range of use cases - Works on following patterns irrespective of templates and Zones |
| Manual Intervention | Manual Intervention is mandatory for every exception (new and old) | Reduced manual intervention required only for new exceptions |
| Accuracy | Lower accuracy - Does not work on improvising the readability of the document | Better Accuracy - Improves readability of the document thereby enhancing capture rate and accuracy |
Up to 99% accurate data
Constantly increasing capture rate
Reduced learning time resulting in faster ROI realization
Better accountability due to confidence scores indicating accuracy of the extracted data
NSEIT is amongst the early adopters of the Intelligent Automation solutions as we understand the BFSI industry closely and have been empowering them to be future ready.
Go to https://nseit.com/services/digital-transformation/business-transformation and understand how you can emancipate from conventional business process approach and step ahead in your digital transformation journey.
NSEIT has enhanced its capabilities by becoming a leading partner of Antworks, an Integrated Automation Platform provider for end-to-end automation. Check out https://nseit.com/alliances_antworks to know more about how we can together enable you for future.
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