Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors

Transform Traditional Claims Processes with Intelligent Document Processing

Nov 14, 2022

Processing claims information and documents is a convoluted process. It requires an entire infrastructure around staffing, training, and business rules set for a manual process, as well as IT systems that support the whole process. It has been quite challenging for insurers to manage claims processing manually or through legacy solutions due to the complexity of the documents. But with increased digitization in the insurance sector, demand for usage of technology is soaring.

Moreover, as the majority of claims information is available in an unstructured format, processing it requires technology that can handle documents with reduced human intervention. Henceforth, a lot of insurers are using template-based OCR for document processing. But its scope is limited as it needs human intervention and highly relies on the structure & layout of the document to be processed. Therefore, market pressures are spurring transformation and demand for seamless document processing. But it’s not so easy. Then how will insurers achieve accurate & efficient document processing? For insurance companies dealing with huge volumes of unstructured claims data, leveraging intelligent document processing solutions can be key to improving process efficiency, decreasing costs, and increasing accuracy.

This blog will take you through the challenges related to traditional template-based OCR processing and how intelligent document processing helps in seamless claims workflow.

Conventional Template-based OCR Challenges

Before we understand how intelligent document processing works, let’s take a look into the difficulties that traditional template-based OCR poses for insurers.

Depends on Input Quality                                          

Text recognition and extraction quality rely on the quality of the picture provided to the engine. For instance, the precision reduces considerably if the character height is less than 20 pixels.

Templates & Rules-Based 

Conventional OCR requires the usage of rules and templates. The template-based OCR system is programmed to take data input from the appropriate fields in accordance with strict rules. Therefore, it is inconvenient for OCR systems to handle various document types and struggles with unstructured ones.

Lack of Automation Scope 

Due to its dependency on rules and templates, traditional OCR lacks several automation options. For example, if you want to extract structured data from bills, you would need a separate rule for each individual data field. And as you know, invoices come in various forms and formats, resulting in numerous difficulties and restrictions.

Adding more rules would require additional training and resources for the OCR engine, and there will always be a need for additional rules. This creates a significant bottleneck.

Not a Very Pocket Friendly Option

Due to additional algorithms and rules required to improve accuracy, conventional OCR may become quite expensive to operate. Besides, the implementation of these rules and algorithms does not always ensure high-quality outcomes, as picture input quality is also a vital factor.

Not Compliant with a Wide Range of Document Types 

If basic papers with few alterations are scanned with standard OCR, the result is frequently very accurate. However, several organizations process a variety of document types. The difficulty increases with the increase in document diversity. Due to the fact that the traditional OCR process is template-based, it cannot keep up with a wide range of documents.

Key Takeaways

  • At first, claims processing takes in unstructured documents and claims forms for data intake.
  • These forms run through a scanner or OCR to extract the required information.
  • Due to the complexity of the forms & unstructured documents, the OCR is capable of extracting data from only 10-15% of the documents.
  • The rest 85-90% of the documents that failed OCR extraction, are sent for manual processing, which consumes a lot of time.
  • The process is slow, expensive, and unable to scale up to match demand fluctuations. 
How Can Intelligent Document Processing Help Insurers to Streamline Claims Processing?

Intelligent Document Processing automates data extraction from unstructured and structured documents, eliminating the need for manual data processing. Unlike template-based OCR, Intelligent Document Processing can manage document variation and complexity using multiple RPA technologies. Henceforth, more & more insurance carriers are deploying Intelligent Document Processing (IDP) in claims workflow in order to improve process automation, which is otherwise impeded by manual data processing bottlenecks and OCR limitations.

Let’s explore a few ways in which your insurance company might profit from Intelligent Document Processing:

  • Improves Productivity

Intelligent document processing streamlines claims and insurance-related data collection, enabling rapid and accurate information input through automation. Moreover, IDP can also classify documents by their types and extract data from structured, semi-structured, and unstructured documents. This eliminates human error & makes the process smoother – all contributing to an increase in the insurance organization’s overall efficiency.

  • Enhances Client Satisfaction

Insurance businesses can use intelligent document processing to speed up and simplify claims data processing. This is particularly useful when handling large amounts of data and various document types. It enables insurers to achieve faster time to market, helps them win new customers, and provides excellent customer service that contributes to enhanced customer satisfaction and retention.

  • Obliterates Tedious & Time-consuming Activities

Leveraging intelligent document processing can automate the tedious manual processes that consume time. This will allow in-house staff to devote more time to crucial high-value tasks.

  • Decreases Operating Expenditure 

Traditional OCR document processing method increases operating expenses and requires extra expenditure as the volume of incoming data grows. Leveraging intelligent document processing helps to reduce these overhead expenses. This will result in improved organizational growth without incurring additional costs.

  • Ensures Compliance

Intelligent document processing can produce higher-quality data as it ensures that data compliance standards are not breached. In addition, the digitization of documents enhances security and makes safety compliance effortless.

Key Takeaways

  • Now, 80-85% of the documents are automated. In rare cases, only some documents are sent for manual correction where only anomalies are presented to the staff, not the whole set of extraction activities.
  • Intelligent document processing decreases the complex claims processing time from days to hours.
  • This process provides an enhanced CX with rapid turnaround time and increased accuracy.
  • The manual workload is significantly minimized, allowing in-house employees to be repurposed.
  • System management is decreased as there is no OCR template management.
Wrapping Up

Intelligent Data Processing enables insurance firms to take a right initial step toward establishing an improved data management procedure and develop an effective automation process for your organization as a whole.

Get in touch with us to understand how we can help you process your insurance documents seamlessly.

 

 

Let’s discuss your project. Connect with us.

sales@kmgus.com

+1 631 777 2424

US Office

420 Jericho Turnpike, Suite 215
Jericho, NY 11753

India Office

Plot 262, Udyog Vihar, Phase IV
Gurgaon 122015, Haryana
Phone  +91 124 4735 555

Get in Touch

I agree to the processing of my personal data and accept the terms of Privacy Policy.