Some of the documents and data that organisations typically process daily include templates, client onboarding, insurance claims, quotations, contracts, sales orders, invoices, customer correspondence, and many others. In order to make sure that an organisation’s digital transformation is a success, they need to manage these documents aptly and convert them into data that can be used for further processing.
With evolving customer expectations and new advancements in digital technologies driving rapid change in this sector, relying on multiple layers of manual processes that make customers wait while employees try to make sense of complex documents is no longer an option. Luckily, there is a means to eliminate the need for humans to perform these tedious tasks.
Intelligent Document Processing (IDP) makes the repetitive and labour-intensive task of manual data capturing and entry much simpler by harnessing technologies such as AI and machine learning to understand and turn unstructured and semi-structured data into a structured format which can be augmented by humans if needed. The reason IDP has gained so much attention in recent times, and perhaps the reason you are reading this article right now, is because it provides disruptive solutions to automate data extraction projects that were previously extremely difficult, if not impossible to solve.
This makes it more practical for businesses that receive high volumes of data - such as documents, emails, and other files across the enterprise - to process and automate the extraction of the relevant data, resulting in increased productivity, automation, and cost reduction by removing manual processing bottlenecks. The extracted data can then be used for relevant purposes, such as invoice payments, sales order processing, KYC updating, and much more.
What sets our solution apart is that, through the use of computer vision, our native and optical character recognition (OCR) extraction is powered by deep learning OCR, which means it is not just another OCR or text extraction tool. Together with a human feedback mechanism, our neural networks allow us to get the most accurate reads and ensure that our system gets better and better over time as it learns.
Our natural language processing (NLP) algorithms help automate any business process which involves the processing of natural language data such as emails, chats, complex documents, and legal contracts, while our predictive, text, and behavioural analytics derives high-quality information from any text format.
Aside from an increased ROI, Sybrin can offer you out-of-the-box pretrained IDP solutions which can be easily reconfigured to suit your requirements, while also streamlining your document tracking by providing you with the ability to view records anytime, anywhere.
Our solution is industry agnostic and can provide the following benefits:
50% to 400%
50% to 75%
Reduction in process cycle time.
25% to 75%
Reduction in cost.
We’ve recently provided our solution to a large insurance company in South Africa. Not only did we provide them with the perfect solution to suit their needs for policy document extraction, but we knocked other big tech companies out of the park.
What the Customer Needed
To automate the extraction of data from policy documents
A hands-off approach.
An outcomes-based approach.
What We Delivered
A solution that automatically extracts, intelligently interprets, and automatically translates data from policy schedules.
Machine-based learning – Model to automatically learn and adapt to changes where possible.
Why We Won
Performed better in the proof of concept.
Provided an end-to-end solution.
Provided a better delivery model.
Who We Beat
Benefits to the Customer
8 hours to 5 mins
Reduced processing time of a policy document.
80 to 8 People
Reduced number of staff needed for this process.
Reduced the average processing cost per policy.
Sybrin’s proven IDP solution is one of the easiest and fastest ways companies can leverage AI to deliver tangible results to increase efficiency, deliver a superior customer experience, and maintain compliance.View Solution »