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Intelligent Automation for Insurance Providers: A Strategic Guide

Intelligent Process Automation and Intelligent Document Processing for Insurance Providers

INDICO RESOURCE

F50 Insurance company achieves 300% return on investment with Indico Data

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Topics covered on this page:

  • Key benefits of insurance process automation
  • How to apply intelligent automation to insurance processes including submission intake, underwriting and claims
  • Problems with the early attempts at insurance process automation, including RPA and OCR
  • Use cases for automation in insurance, including claims, underwriting submission, compliance, customer onboarding and healthcare

Insurance providers have long struggled to automate insurance processes involving unstructured documents. But the situation is changing for the better. Now intelligent intake solutions enable insurance companies to:

• Automate commercial insurance submission processes
• Transform underwriting functions
• Reduce time to bind and underwriting leakage
• Automate insurance submission triage processes
• Improve loss ratios
• Automate claims intake processing

Intelligent intake takes advantage of AI technology that enables them to “read” emails and other unstructured documents much like your employees do – only much faster and with improved accuracy.

Intelligent automation for commercial insurance processes

Intelligent document processing technology can understand document context much like a human does - so long as it’s based on a model that incorporates a significant number (as in hundreds of millions) of labeled data points, enough to enable it to understand human language and context.

Having a large set of data to “train” brings intelligence to any artificial intelligence solution. But to utilize AI in insurance, even the largest insurance companies would be hard-pressed to create their own model based on that much data.

Applying artificial intelligence to insurance processes
Advanced intelligent document processing solutions employ artificial intelligence technologies including natural language processing, machine learning and transfer learning. Together, they enable employees who process insurance documents day-to-day to build process automation models – without involving data scientists or even IT. That helps deliver a higher level of model accuracy and significant scalability in terms of an insurer’s ability to roll out automation models across the company.

Insurance automation: email processing
Chief among the areas that are ripe for automated claims processing is the corporate email in-box. It’s common for insurance providers to have one or more email addresses to which clients send underwriting submissions and claims information.

Many of these emails contain not only potentially complex requests, but attachments, such as ACORD forms, loss-run reports, spreadsheets, custom forms, perhaps photos and more. An intelligent intake solution can essentially read and triage each email, determining where each should be routed.

The tool can also extract and read attachments, often dealing with them on its own. The Indico Unstructured Data Platform, for example, includes a large library of ACORD forms, often resulting in straight-through processing of the forms. In general, the Indico Data solution can ensure each email is entered to the correct workflow for efficient, automated processing.

Automating first notice of loss (FNOL)
No matter how an insurance provider receives word of a client claim, it’s sure to come with plenty of documentation. Much of it will be unstructured, making it beyond the scope of a robotic process automation solution.

An intelligent document processing tool, however, can process any type of document, structured or unstructured. In the insurance first notice of loss scenario, that may mean accepting the initial claim (the FNOL), then validating the claim is covered by the client’s policy. Much of that process can be automated with a tool that “reads” the claim form, extracts pertinent data and inputs it to a claims management tool. Here again, STP may apply to simple claims while others can be prepared with all pertinent information already assembled for an adjuster, greatly reducing time spent on the assessment process.

Meet compliance requirements with explainable AI
A key concern in the highly regulated insurance industry is the ability to explain why an automated, AI-based solution makes the decisions it does. If a claim is rejected, the insurance provider must be able to explain in simple terms the reason behind that decision. A sound intelligent automation tool will make that easy, with an audit trail that makes clear – in plain English – the rationale behind each claim decision. Additionally, if the line-of-business insurance underwriting or claims process owners create the automated models themselves, the models reflect the way they naturally work during the claims assessment process.

Outsmart insurance fraud perpetrators
Automation is crucial for insurance providers if they are to keep up with insurtech startups, or simply compete effectively vs. traditional competitors. It’s also important to help detect fraud.

More than 7,000 insurance providers collect over $1 trillion in premiums each year, the FBI estimates, making them a prime target for illegal activity. The total cost of insurance fraud exceeds $40 billion per year, the FBI says – and that doesn’t include health insurance. That costs the average U.S. family between $400 and $700 per year in increased premiums.

Problems with the early attempts at insurance process automation

Documents that contain unstructured data present problems for most insurance providers’ policy automation solutions because they are not easily digestible by computers.

Claims processing, for example, often involves a review of notes from an adjuster based on conversations with the claimant. The adjuster’s notes are primarily free-form, following no standard format, with plenty of variation from one adjuster to the next. An adjuster’s file may also include photos, along with reports from doctors and lawyers. In short, it’s a significant amount of data, nearly all of it unstructured - and hence a prime candidate for claims process automation.

To process a claim, someone has to pore through hundreds of pages of documents, extract pertinent bits of information and input them into a downstream claims processing system. Relevant data may include the claim number, policy number, date and time of loss, location, coverage limits, and more.

That data entry job is labor-intensive and time-consuming, not to mention error-prone, making it ripe for insurance document automation. Companies have tried using insurance process automation tools based on keywords and rules, with less-than-stellar results.

Keyword and rule-based approaches to insurance automation use templates that define precisely where the data you want to extract is located in a given document, along with a slew of rules defining what data to extract and what to do with it.

In practice, an insurer often hires a consulting firm to write countless rules and templates to try to account for every variation in the documents the company needs to process. That may work, briefly – until a document comes along that doesn’t neatly fit into any of the rules or templates the consultants created. Once this happens, the entire system breaks down - and that's why you need to deploy intelligent automation in insurance.

If you think of an insurance adjuster’s notes, it’s easy to see how it won’t take long at all until a new type of document comes along, making the rule-based approach all but futile. On top of that, it’s horrifically costly, whether you use outside consultants or in-house resources.

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What about OCR and RPA?

It’s not unusual to hear about optical character recognition (OCR) as a solution for automating insurance underwriting and claims processing. But by itself, OCR can’t effectively deal with the unstructured data that is the hallmark of insurance documents.

OCR is machine learning technology that can convert documents such as PDFs into a machine-readable format. That’s useful, but it still leaves you dealing with templates to extract pertinent information.

Robotic process automation suffers from much the same problem when it comes to insurance automation use casesRPA is great at automating processes that involve the exact same steps each time. Say, for instance, an insurance data entry clerk entered the exact same keystrokes in the same order time after time into a claims processing system. That would be a process that’s ripe for RPA.

But, as explained above, that’s not at all how insurance processes work. Rather, they require a human being to make judgment calls about which data to extract and enter. Any insurance process that involves unstructured documents – which is most of them – will suffer the same problem. (RPA can, however, complement intelligent intake solutions in insurance process automation. More on that here.)

Intelligent automation in insurance: Use cases

Claims Processing
Insurance claims automation is a common intelligent intake use case . Claims automation in insurance can automatically classify and annotate a new claim and route it to the right SME for evaluation and processing. This results in faster turnaround time and improved accuracy for a processed claim, driving improved customer satisfaction and organizational efficiency.

Commercial Insurance Underwriting Submission Processes
Often involving thousands of pages of documentation, major commercial underwriting processes can be dramatically improved with intelligent intake solutions. Such a solution can read underwriting submission emails, unbundle and sort all attachments, then extract pertinent data and enter it into downstream processing systems such as Duck Creek and Guidewire. The process includes extracting data from loss run reports to glean a highly accurate picture of the applicant’s loss history, helping to ensure favorable loss ratios.

Appraisal Processes
Whether performed before writing a policy to determine property value or after a claim to determine compensation, insurance appraisal processes can involve many unstructured documents. An initial appraisal for property value may include receipts, purchase and sale agreements, and images, while claims have contractor estimates and more. An IDP solution can deal with each type of document and help companies automate the extraction of relevant data. This offers another opportunity for insurance document automation.

Regulatory Compliance
In a highly regulated industry with dozens of state and federal regulatory bodies, promptly responding to regulatory inquiries represents a significant expense for most insurance providers. Intelligent automation can create augmented responses to inquiries, dramatically reducing the response times and resources required.

Enrollment and Customer Onboarding Processes
Getting new clients is a good thing, but it also creates a challenge for insurance providers: processing all the required documents. To date, onboarding new clients has been a largely manual process that for large insurers can easily involve 15 million documents per year, making it a ripe target for intelligent document processing technology.

Healthcare
Few vertical industries are as document-intensive as healthcare, whether on the provider or insurance side. That makes healthcare insurance providers ripe for tools that automate insurance claims processing and other tasks, driving cost savings and improving the patient experience.

Related content: How partnerships help drive underwriting transformation and automate the insurance submission process

How Intelligent Intake complements RPA

Some insurance automation use cases in life and health insurance may involve both robotic process automation and intelligent intake as complementary technologies. RPA is great at automating repetitive tasks to make a process less labor-intensive for humans and works well with deterministic business processes that involve structured data. Intelligent intake or IDP solutions, on the other hand, can automate processes that involve unstructured data. A common is to use IDP to “read” unstructured data and translate it into a structured format that an RPA tool can then process. For example, the RPA ingests documents and sends them to the IDP solution for classification and data extraction. Once extracted, the IDP tool puts the data into a structured format that the RPA tool can work with, such as a spreadsheet. The RPA tool can then take in the now-structured data and populate a downstream system, such as a customer relationship management (CRM) tool.

Related content: How to automate the insurance submissions intake process and drive new premium growth

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