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Automated Life Insurance Underwriting and Claims

Automated Life Insurance Underwriting and Claims Intake

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Many aspects of the life insurance business require providers to process documents of various types, most of them unstructured. Processes including application intake, underwriting, servicing, claims processing, and regulatory compliance all require humans to read myriad documents and extract relevant information for input into downstream processing systems. Such processes are both time-consuming and prone to error, making them ripe for automation.

Providers have turned to robotic process automation (RPA) for their life insurance policy processing, as well as other automation approaches involving templates and optical character recognition. But they’ve quickly found such methods only get them so far – because they can’t effectively deal with unstructured data.

What’s required is a solution that adds artificial intelligence (AI) to life insurance automation. AI is at the foundation of intelligent intake solutions that can “read” unstructured documents just as a human does. Such a solution brings true automation to life insurance processes, freeing up employee time and accelerating processes while reducing errors.

Related content: Why a F50 insurance company expects to save $100M with the Indico Platform


Download the eBook: Unlocking the value of unstructured data for Insurance

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Life Insurance Process Automation Key Benefits

Capacity Expansion

Increase revenue – but not costs

Increase Competitiveness

With stalwarts and fin-tech startups alike

Improve Efficiency

free up employee time for higher-value initiatives

Robotic process automation in life insurance

To date, many document automation projects in life insurance have involved robotic process automation. RPA is indeed useful for simple automation routines that involve highly structured documents.

The problem is, use of RPA in life insurance hits the wall when it comes to the sorts of unstructured documents that make up 80% or more of all life insurance documents.

The life insurance application intake and underwriting process is one example. It involves collecting various data and documents regarding the health of the applicant, including:

  • Attending Physician Statement (APS)
  • Medical Information Bureau (MIB) check
  • Motor Vehicle Report (MRV)
  • Hand-written, open-ended answers to questionnaires

Historically, data entry teams read each document and input relevant data into a downstream life insurance processing system. Data entry, of course, is a time-consuming, labor-intensive, monotonous job, not to mention prone to error – making it a candidate for automation.

But given the information to be collected varies for each applicant, and that it comes in different formats from multiple sources, it’s all but impossible to come up with a single RPA template that covers all possible variations. Similarly, it would be impractical, and exceedingly expensive, to try to come up with templates for each type of document that may be involved in the process. That makes RPA of limited use in life insurance underwriting.

Related content: How intelligent automation speeds up insurance underwriting and claims processing

Intelligent Intake for Life Insurance Providers

What’s required to deal with unstructured data is an automation approach that has cognitive capabilities that give it the ability to “read” any kind of document much like a human would. The underpinning of these capabilities is a vast collection of labeled data points, which serve to give the tool context behind most any kind of unstructured data.

Indico’s intelligent intake platform, for example, sits atop a database of some 500 million labeled data points. Even the largest life insurance firms would need to spend years, and large sums of money, to collect and label that much data.

Artificial intelligence (AI) technology known as transfer learning then enables life insurance providers to take that vast database and build their own models to address specific requirements – without having to know anything about AI.

Instead, the business people on the front lines, who know the processes best, use intuitive tools to label maybe 200 documents, indicating which pieces of data they want to extract. That’s enough to train an automation model to work with an accuracy rate of about 95%.

Not your Typical AI

If that sounds different from other life insurance AI solutions you’ve encountered, that’s because it is. While Indico’s intelligent intake solution is certainly sophisticated in its use of cognitive technologies including machine learning and natural language processing, we keep the artificial intelligence technology behind the scenes, enabling an army of citizen data scientists to use the technology to solve real business problems.

Natural language processing (NLP), for example, is core to our platform. It’s what enables our generalized model to understand the context around unstructured data, just as a human would. But it’s built into our models and functions behind the scenes; there’s no need for those who use the platform to even know what NLP is.

The same goes for machine learning (ML). While our engineering team built our intelligent intake platform using cutting edge ML models, they all sit in the background – there’s no need for users to tweak or otherwise interact with them, or even understand how they work. Citizen data scientists instead can just think about how to apply the platform to take repetition and complexity out of their processes and deliver real business benefits.

In short, an intelligent intake platform enables you to introduce artificial intelligence to life insurance underwriting, claims, and other processes – without requiring expensive data scientists with experience in AI.

Intelligent Document Processing Use Cases:

Automated life insurance underwriting

Automated life insurance underwriting is a prime use case, given the many and varied types of documents involved. To assess the risk of a potential client, life insurance companies must collect data on health risks, including potentially numerous documents from myriad healthcare providers. Insurers also need to assess an applicant’s net worth and creditworthiness, which means examining various financial documents. Collecting all this information and pulling out appropriate data is time-consuming and prone to errors. Using artificial intelligence for life insurance underwriting can dramatically speed up functions including document assessment and data extraction, assessment of loss runs, and review of the customer’s claim history – all important factors in the underwriting decision.

Automated life insurance regulatory compliance

Life insurance companies are highly regulated and subject to a constantly changing regulatory landscape. Yet failure to comply may mean fines as well as operational and reputational damage. Intelligent document processing for life insurance can help companies stay in compliance by automating many routine tasks, dramatically reducing the possibility of human error that could put compliance at risk. Automated processes also leave behind a log of all actions, which will prove valuable should the company be subject to an external audit. Automation can likewise help with generating regulatory reports and compliance checking processes.


Life insurance claims automation

Processing life insurance claims likewise involves collecting and assessing numerous unstructured documents, including a claim form, death certificate, original policy document and medical reports. Intelligent document processing can help insurance companies implement an automated claims processing workflow that includes extracting pertinent data from all documents, assessing whether the applicant is indeed a qualified beneficiary and if the policy is still in force and active

Automated life insurance policy management

Life insurance policies can change over time, when policy holders move or request limit increases, for example. Intelligent document processing can automate many routine insurance policy management tasks, such as with models that pull change of address requests from emails and transcripts of voice calls – and kick off scripts that complete the request. Intelligent models can also automate tasks such as processing of loss run reports, analyzing statement of value reports, and more.

Regulatory compliance

Extract relevant data from insurance documents and feed it to an analytics engine to identify any areas where you may be running afoul of regulatory requirements. Indico Data can also help index data to simplify and speed responses to regulatory bodies.

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Life Insurance Process Automation: Key Benefits

4x increase in process capacity
Intelligent document processing in life insurance enables underwriters, adjusters, investigators and others to be more productive, getting more work done in the same amount of time.

85% faster cycle time
Automation decreases process cycle times by as much as 85% while improving accuracy, which leads to improved customer satisfaction and reduced costs.

8o% increase in efficiency 
Automating life insurance processes frees up time for employees to focus on more rewarding and strategic work, increasing both employee satisfaction and company competitiveness.

Ensure compliance 
Improving accuracy in life insurance processes helps ensure companies stay in compliance with industry regulations, while leaving a valuable audit trail.

Improve competitiveness 
Process automation ultimately makes life insurance companies more competitive, whether the competition is an industry stalwart or an insuretech startup.

Improve application integration 
Intelligent document processing of life insurance processes often includes integrations with various important applications, including business process management, enterprise resource planning and customer relationship management tools, thus streamlining operations.

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