Automating insurance claims intake processing

When it comes to claims processing, commercial insurance companies historically have been forced to choose between speed and accuracy.

Choosing speed means increased risk of errors and non-compliance, making hasty adjudication decisions that can affect profitability, and negatively impacting the customer experience if things go awry.

But choosing accuracy means increasing the risk of losing out to more nimble competitors that offer a faster customer experience, as well as frustrating customers with long settlement times. That, in turn, negatively impacts your
net promoter score (NPS), ultimately leading to customers jumping ship.

“Claims dissatisfaction is a major factor in driving policyholders to switch to another company, with 74% of dissatisfied customers either saying they did change providers (26%) or are considering it (48%),” according to a 2022 Accenture study.

Learn how a F50 Insurance company achieves 300% return on investment with Indico Data.

Part of the reason intelligent claims intake delivers a 70% reduction in manual document handling for claims is
integration with downstream insurance automation platforms such as Guidewire
ClaimCenter. Today that’s a familiar swivel chair process, where a claims handler reads a claims email
and attachments, looking for data to enter into Guidewire, including case owner, case status number, cause and date of loss, estimates for extent of loss, and the like.

With an intelligent intake solution such as Indico’s, AI models can “read” claim emails and attachments and pre-populate some 80% of the required fields in Guidewire ClaimCenter. This kind of automated claims processing not only dramatically speeds processing time but increases accuracy – because computers don’t get tired and make mistakes like people do. In fact, intelligent intake solutions that support staggered loop machine learning become more accurate over time by learning from when users accept or correct a model’s predictions and classifications.

View Episode 9 of Unstructured Unlocked, where Indico Data’s Christopher Wells talks with
Steven Weiss, former Senior Vice President and Chief Underwriting Officer at Munich Re Specialty Group Insurance Service.

The traditional claims handling process was highly manual. For the initial claims reporting, a client or broker submits a claim by phone or email, triggering a potentially lengthy process to gather all relevant details. This includes collecting documents and images detailing the damage, estimates on repairs, any injury data where applicable, and so on. Often an on-site evaluation is required to assess and determine the validity of the claim, generating potentially hand-written adjuster notes. At some point, a fraud detection process is also initiated, again a highly manual process.

For many years carriers have been trying to automate claims processing by using optical carrier recognition (OCR) technology to help process documents by converting hand-written and printed text to a machine-readable format. While an improvement, OCR required templates to read most documents, making it vulnerable to errors when a given piece of data was not in the exact spot the template expected it to be. That created a costly process of near-constant template maintenance to keep up with new or changed documents. Given that, along with the need for manual review for accuracy, OCR could hardly be considered a solution for insurance claims automation.

Intelligent intake represents a quantum leap forward in insurance claims automation. A form of intelligent document processing, intelligent intake obviates the need for templates. Instead, it uses artificial intelligence technologies, including large language models, natural language processing, machine learning, and transfer learning. Taken together, these technologies enable insurance companies to build intelligent intake models capable of ingesting nearly any kind of document or image and extracting relevant data from it. That data is then converted into a structured format that can be entered into downstream claims processing systems, such as Guidewire Claim Center.

The ability to ingest unstructured documents, extract relevant info from client claims and turn it into a structured format represents a significant step in insurance claim process automation, and gives rise to numerous important capabilities and use cases.

A key concern in a highly regulated industry like insurance is the ability to explain why an automated, AI-based solution makes the decisions it does. If a claim is rejected, the insurance company must be able to explain in simple terms the reason behind that decision. A sound intelligent intake tool will make that easy, with an audit trail that makes clear – in plain English – the rationale behind each claim decision. Additionally, given insurance claims handlers create the automated models, the AI models naturally reflect the way these professionals think during the claims assessment process.

Read our blog: How intelligent automation speeds up insurance underwriting and claims processing.