Pacing ahead to conquer the next-gen technology & drive business growth
Automation in some form or the other has been in conversations for past few years across industries. Organizations who understand the benefits of automation are implementing it as a part of their digital transformation journey as they realize it to be a means of being competitive.Today, for many of us automation has become synonymous to a technology called RPA (Robotic Process Automation).
RPA has been implemented across multiple industries and geographies to automate simple, rule-based, monotonous business processes.
While RPA has significantly improved productivity leading to faster and visible cost reductions, scaling this solution to an enterprise level and creating impactful economical value is yet to take place. This is mainly due to the limitations of RPA not being able to automate processes end-to-end.
Start point of every business process is data and enormous amount of efforts are invested in data extraction and cleansing to convert them into appropriate formats as the business process demands. This manual effort on data is the main hindrance to end-to-end automation.
Data is available in multiple formats like pdfs, scanned documents, handwritten documents and images. Currently, all RPA tools use OCR (Optical Character Recognition) engines to extract data from several formats of documents. OCR operates on the concept of detecting patterns of characters based on what is stored on its database. This restricts the ability of an OCR to understand even a slight deviation in the characters reducing the accuracy of the data extracted.
Today customer’s demands are moulding the automation tools and technology. OEMs are upgrading their products and integrating themselves with multiple other tools to enhance their functionalities based on customer’s requirements. Data extraction from innumerable quality and formats of documents is one such customer requirement that has not been met with any of the OCRs entirely.
Since OCRs are unable to extract the data accurately, RPA cannot be applied unless the data is manually prepared, limiting the scope of automation. To broaden the scope of automation, there is a need of a technology platform that is not only capable of extracting the data based on what its existing database engine has, but also be able to learn and adapt, like we humans do.
For example, if a letter “s” is written in a slightly different manner which the tool is unable to recognize, then it should be able to learn and add this variant of the letter in its database, so the next time it encounters a similar variant, it is able to extract it as letter “s”.
Intelligent data extraction is based on the concept of evolving by learning and adapting. As more and more data formats are processed, better is the learning curve and productivity.
Intelligent Document Processing operates on the AI technologies of Machine Learning (ML) and Natural Language Processing (NLP) and takes the document processing journey through following stages:
Data Digitization (of unstructured data) - Unstructured data available in diverse formats needs to be first digitised. Digitization means the input scanned documents and images need to be converted into text that machine can read and understand.
Data Interpretation - Ability to contextualise and interpret the meaning out of data extracted in the step above by using Natural Language Processing (NLP).
Data Mobility – Integrate the meaningful data with technologies like RPA, API and micro-services to achieve end-to-end intelligent process automation.
Intelligent Document Processing engine can extract data from all kinds of structured / unstructured data sources like readable pdfs, scanned images, handwritten text, cursive handwriting, photographs and signatures. It has two main components that enhances the quality and accuracy of the data extracted:
Image enhancer - It enhances the readability of the image by cleaning unwanted elements (noise) on the document and paves way for greater accuracy of extracted data.
Auto Indexer - Data extraction is preceded by an important step of classification of documents and its contents as per business rules of a particular business process. With Antworks AI-driven models, it is as simple as a click of a button to classify the documents and index them in a way that makes document searching extremely easy and fast.
Gartner predicts that with a continued growth in IT environments, data management practices will evolve and require more resources to process the data. In such scenarios, there will be a need for Artificial Intelligence (AI) based technologies for advanced automation and workload management.
AI and RPA are implemented in most of the organizations, but in silos. However, they could be more productive if implemented together.
With NSEIT’s established ecosystem of emerging technologies and solutions, our customers have been able to successfully embark on their digital transformation initiatives and achieve enterprise wide benefits like:
“Straight through processing” - Higher probability of end-to end Intelligent Automation
Enhanced productivity and better utilisation of human resources
Enterprise wide cost reductions leading to economic growth
Boost Customer Experience
NSEIT is a leading technology partner for BFSI players in India, US & Middle East. Check out https://nseit.com/services/digital-transformation/business-transformation to understand how NSEIT is empowering the leading financial institutions to drive transformation and meet customer demands.
NSEIT has also partnered with Antworks to bring in “top of the class” data extraction capabilities through Artificial Intelligence based Integrated Automation Platform. Check out https://nseit.com/alliances_antworks to know more on how you can achieve end-to-end automation.
Presales - Digital Transformation
Associated with NSEIT for around 2 years, and with overall 8+ years of experience, Kriti has been managing end-to-end presales and alliance for multiple digital transformation technologies like Intelligent Process Automation, Business Process Management and Low-code / No-Code Development across Banking, Insurance and Financial Services Market Space.
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