OCR In Insurance: Automating for Digital Transformation
Integrating OCR and IDP allows the insurance industry to gain a huge advantage as they can help automate document processing, a process that plays a significant role in the industry.
Document processing involves manually converting images into structured data that can be used directly by other software. With the help of OCR & AI, automatic document processing becomes possible.
Converting physical documents into digital formats is an essential step in most companies' digital transformation. However, it requires thoughtful planning and the correct document processing solution to successfully accomplish this.
All businesses have to deal with documents with various formats and value-specific information. The old school approach of manual document processing involves a person identifying specific data fields and typing them into spreadsheets or software for downstream processing. This process can be quite time-consuming, expensive, and prone to human error. As technology advances, businesses have incorporated different intelligent solutions to help them optimize and automate business processes, one of which being Intelligent Document Processing (IDP) solutions.
Document processing has become more efficient in the last few years with the introduction and implementation of AI and other software built to ease use for the end user. A multitude of businesses is still bogged down and troubled by document-centric processes when a simple solution has emerged in the form of intelligent document processing.
Intelligent Document Processing solutions are here to help businesses extract data from unstructured, semi-structured, or complex documents. Let's dive into what document processing is, why we do it, and how we can do it better.
Document processing initially was a sort of production line work style that aimed to sort and extract data from physical documents. In layman's terms, document processing takes analog data and manual forms from a physical form into a digital format so that the data trapped in said physical documentation can then be fully integrated into day-to-day business practices. By using document processing systems to extract data, companies can digitally replicate and recreate the document's original layout, text, images, and structure. It is ideally used for converting documents with identical formats.
Document processing can be performed in a multitude of ways: manual data entry, computer vision algorithms, neural networks, etc. The typical step-by-step process of digitizing analog data into digital data is as follows:
These document processing solutions are rules-driven. Programmers usually develop some sort of extraction rules that define the category and formats of their documentation when they are developing document processing solutions. Once all of these rules are defined, the data extraction can begin and the layout and structure can be pulled forward.
There are several methods that can be utilized for extraction. This step usually involves optical character recognition (OCR) recognizing the texts and then converting them into machine-encoded texts that are easily searchable instead of imagery. Another format would be through intelligent character recognition, a form of handwritten text recognition (HTR), which can recognize standard text as well as various fonts and styles of handwriting.
The extracted results will be reviewed by the staff to make sure that there isn't any error. When the format of a document cannot be processed or errors are identified, things will be flagged for manual review and then fixed through manual entry.
In this last step, the final results are stored in formats that can then be used with other applications.
Intelligent Document Processing (IDP) uses machine learning and AI-powered automation to enhance document classification, information extraction, and data validation. By training the extraction models with enough amount of samples, intelligent document processing solutions will be able to understand the context of the documents rather than blindly extracting all texts within images. Furthermore, IDP solutions also include pre-processing and post-processing scripts to maximize the accuracy of data extraction.
These technologies allow intelligent document processing solutions to handle documents with unstructured or semi-structured data, which was not possible with manual document processing. Businesses can benefit significantly from incorporating IDP solutions into business processes as roughly 80% of business data is trapped in unstructured data formats such as physical documents, PDFs, images, and emails.
After converting documents to structured data, IDP solutions can be easily integrated with other software or robotic process automation (RPA) solutions so that the extracted data can be further analyzed or used for automated workflow.
It may take days or weeks for a team to process piles upon piles of documents if done manually. Powerful IDP software, on the other hand, can do that same amount of work within a few hours and possibly even within minutes. By eliminating manual processes and reducing human errors, businesses can process documents faster and more efficiently. Furthermore, since the end results can be directly sent to other software for analysis or processing, the entire workflow is significantly shortened.
With the help of machine learning, pre-processing, and post-processing processes, intelligent document processing solutions can achieve up to 99% of data extraction accuracy, which is certainly much higher than manual data entry. Instead of performing repetitive data entry tasks, staff can simply verify the end results to make sure that the extracted data has no errors.
Automating document processing with IDP allows businesses to reduce costs in a few ways. Firstly, they will not need to hire more staff to manually convert the images to structured data or outsource it to external party. Secondly, businesses can save up physical space to store all the documents before they are processed. Lastly, errors in data, especially for accounting and finance purposes, can sometimes lead to significant financial loss.
By converting physical documents to structured data, businesses can better protect the documents. Digitized documents can be easily backed up on cloud servers and businesses can also ensure that only the right personnel have access to certain datasets. This is especially critical to industries such as financial services or healthcare, where there are immensely strict compliance policies and security regulations.
IDP makes different business processes extremely faster by removing the document processing bottleneck. Users would no longer have to fill out their personal information when they want to open a bank account, for instance. All they would have to do is upload an image of their driver's license or identity card and the IDP solution would extract the needed information and send it along through the system. This then provides a better overall experience for both the clients and employees.
Intelligent document processing essentially captures, extracts, and processes data from various documents. The core characteristics of an IDP platform are:
Although they might vary, the essential steps of any IDP should have:
This can be as simple as taking a photo of a document or scanning it with a printer. However, organizations often need to process a much larger volume of documents more efficiently than those methods. To do this, IDP solutions are very often connected to high-speed and high definition scanning hardware to help digitize physical documents for further extraction.
The quality of the uploaded images has a significant effect on the accuracy of the outcomes of the data input. To help maximize this accuracy, images are often optimized for contrast, skew correction, lighting conditions, etc. Some pre-processing techniques applied include noise removal or reduction, thinning and skeletonization, skew correction, or binarization, amongst a few.
This is the most essential stage of intelligent document processing. This stage involves using OCR engines to recognize text and machine learning models to search for and obtain specific information from the documents, such as date, total amount, unit price, etc. These IDP solutions are typically able to integrate with a variety of different OCR engines and include a plethora of machine learning models that are specifically tailored and engineered to extract information from different documents.
To help increase the accuracy, these models can be pre-trained with a wide variety of samples, or users can actually work to increase the accuracy themselves if they provide enough samples in the specific formats that they are looking to use.
After the data extraction process has been completed, the end results are then validated and verified to make sure that the accuracy of the data is precise. This is done through a series of manual or automated checks before the results are then sent on to further systems or software for processing.
This is the final stage of the IDP workflow, where the IDP system is integrated with other software. This software includes robotic process automation (RPA), enterprise resource planning (ERP), and other software to help automate various business processes. If direct integration into other software systems is not possible, IDP solutions can also provide .CSV or .JSON files that can then be imported and integrated directly with other systems.
When looking for an IDP software solution for your organization, you want to find the one best suited to your needs. Ideally, the IDP platform you choose should have and be able to do the following:
You should have document capture capabilities that integrate with your scanning hardware to move your physical documents to digital formats.
For loyalty programs, customers can upload receipt images and IDP can extract the data from those receipts and send it to the loyalty program app for customers to accumulate loyalty points.
The modern business ecosystem can only thrive with a catalyst involved to expedite workflows and those, in regard to IDP solutions, are disruption, innovation, and evolution. Data is the tool that plays the most meaningful role in the transformation journey.
By finding new and novel analysis methods and gaining new data sources through the implementation of IDP, companies have new and valuable insights that required for them to cultivate and switch to the digital transformation. Data is the crux of 'going digital' and that transformation has created new capabilities, operating models, products, and value propositions to attain that new, disruptive success for every enterprise and organization.
FormX is an Intelligent Document Processing solution that combines a multitude of different technologies, including Machine Learning, OCR, and other AI technologies to help automate document processing for businesses across a variety of industries, such as charities, retailers, governments, caregiver services and many more.
FormX includes a variety of preconfigured data extraction models on business certificates, identity cards, receipts, and other common business documents to help enterprises and organizations automate document processing. Additionally, FormX can develop custom models for organizations with high extraction accuracy upon request. FormX can also be readily integrated with other applications to automate and expedite your business processes.