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.