Insurance is traditionally one of those industries that struggle with an overwhelming volume of paper-based operations. Is there a way to overcome it?
Paper-based processes are greatly dependent on the humans who operate them. Thus insurers operating cost increases and efficiency decreases. Each paper given is destined to become a digital version where the information is out of it manually.
The paper way, which was previously the only way can lead now to several problems: inefficiency, increased costs, human error, lack of transparency, poor customer experience and so on. These problems can impact the overall effectiveness and efficiency of the insurance industry, leading to increased costs and decreased customer satisfaction.
Opportunities of Artificial Intelligence in Insurance
There are not many numbers regarding process automation in the insurance industry, however, the latest data from research companies like McKinsey shows that a significant portion of the industry still relies on manual, paper-based processes, particularly in the claims and underwriting departments.
Claims processing requires the information to be precisely and accurately handled and stored in a database. Claims management process, when properly automated, can increase company effectiveness by up to 80% according to the Swiss insure tech hub!
White label AI solution for Data Extraction from Tensorway can literally save Insurance Companies from a very mean enemy - document duplication! In the insurance industry, excessive copying of documents can be a major problem, particularly during the claims process. Here are a few areas where excessive document duplication can occur:
- Claim Initiation: When a customer files a claim, they often need to provide multiple copies of the same document, such as medical records, accident reports, and receipts.
- Underwriting: During the underwriting process, insurance companies often require extensive documentation, including medical records, criminal background checks, and employment history. This can result in a large amount of document duplication.
- Claim Assessment: When assessing a claim, insurance companies often request additional documentation, such as police reports or witness statements. This can lead to duplication of even more documents.
- Payment Processing: In order to process a payment, insurance companies often need to verify the authenticity of the documents provided by the claimant, leading to a significant amount of document duplication.
These processes can result in a large amount of document duplication, causing delays, increasing operational costs, and potentially leading to human error. This highlights the need for insurance companies to adopt more efficient and streamlined processes, such as digital document management systems, to reduce the amount of excessive document duplication.
Why is Data Extraction a difficult problem to solve for Insurance Companies?
The volume of paperwork that insurance agents handle always implies the enormous variety of layouts and the huge range of documents.
However, there ought to be a way out. Since text can be extracted from a scanned document, it should be possible. But…
The existing market-available solutions have limitations due to the underlying OCR methodology. This methodology, which is now widely used to automate data extraction processes, prevents existing market solutions from being trained to handle problems of diversity and complexity needed for insurance companies.
First of all, OCR method isn't really accurate. Second, it is not taught to extract certain data or evaluate document structure, making it difficult to use it to extract specific fields from documents with various layouts. OCR is unable to distinguish between sender and recipient, buyer and payer, individual tax identification number and business registration number.
OCR outputs can be used to create a solution, but this involves programmatic handling of each new document layout and case, which costs time and money.
In the case of Insurance Companies that work with hundreds of different document types every day, using OCR for automating document recognition processes is almost unrealistic, as it would require literally teams of programmers which would post-process OCR outputs for more and more different new layouts.
The Tensorway approach that we provide here has many advantages over more basic OCR methods. Our method is similar to OCR, yet fundamentally different because it is trained to extract particular text fields from a document. With this method, the program can extract any text fields from the documents written in practically any known language. With this approach, you don’t have to suffer with post-processing and trying to guess which of two detected registration numbers have to be used. Our model is able to adopt new languages rapidly and perform operations with them.
The data will be effectively extracted even if the document is written in a different language but follows the same logic because the model has been taught to understand it.
Our model distinguishes various fields, languages, and document formats correctly!
Additionally, for any purpose given, our model extracts all essential document information. We only need the data from your company to continue training it. By asking this we assume that your company has processes and supporting software where data is saved and managed.
The key to success of our Data Extraction White Label Solution is that it is layout- and language-agnostic. Request more information!
However, our model might be much more helpful because it functions with both scanned PDFs and images!
Rarely do people take pictures of their documents in a good light. You name it: rotated, distorted images, bad lighting, several documents in one image, there are many cases of possible distortions. But, unlike any other OCR based products, our AI based Data Extraction solution does not experience any difficulties by extraction the data. No case is too difficult for a model trained by Tensorway experts!
Learn how you can integrate our methodology into your process right now!
By the way, we can also help you to integrate our solution into your existing system and even develop a new Document Management System for your insurance corporation or company from scratch!
Parent company Anadea has been on the market for over 2 decades. Solid reputation, deep technical and vast project management expertise makes our parental company #1 choice for the most innovative and challenging projects. Flexibility and well established processes make it possible to add Anadea team to practically any stage of development and/ or implementation of our solutions.
Along with cutting edge offers like AI/ML/DL we can brag about the most requested technologies like Ruby on Rails, React, Python and so on. We encourage you to contact us and learn more about our cooperation possibilities!
White Label AI
In the AI world, white label AI solutions are ready-made solutions sold under different brand names.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) is a method for recognizing and reading text in images with Computer Vision technology.
Data extraction using AI refers to the automatic identification and extraction of relevant information from unstructured or semi-structured data sources, such as text documents or images.