By Ajmal (AJ) Malik,VP and Product Manager for the Life Digital Suite at EXL
Both of these are artifacts of an era in which life insurance was maligned as a product that was not terribly well suited to a customers’ real-world needs and one in which the sign-up and onboarding process consisted of a gauntlet of time-consuming chores. On average, it still takes upwards of six weeks of shuffling paperwork, blood draws from a visiting nurse and time consuming underwriting processes before an individual life insurance policy can be initiated.
It does not have to be that way. New advances in generative artificial intelligence (GenAI), which make it possible for insurers to analyze information, assess risk and fine tune individual policy components are making it possible to slash the administrative burden while allowing policies to be tailored to individual customer needs.
In our work developing underwriting and analytics technologies with some of the largest life insurance carriers, we’ve been able to identify three key areas where implementing GenAI is making it possible to take the guesswork and the grunt work out of the underwriting process.
Organizing Unstructured Data
One of the biggest operational challenges in the life insurance underwriting process is accessing, organizing and extracting key data from a ragtag assortment of medical records, electronic applications, EHR data from multiple different systems, existing insurance information, and other information that can be used to assess risk. This is a pain point for insurers and prospective customers who must often track down and share information multiple times. On any given policy, a prospective customer may be asked to provide financial documentation, fluid samples, proof of a gym membership and countless other pieces of information that invariably need to be faxed, scanned, compressed and sorted just to get the process started. That’s not exactly the warmest welcome for a prospective customer who has just expressed interest in your services.
New GenAI-powered data extraction tools are making it possible to take much of the pain out of that process. By automatically extracting key pieces of customer information from multiple different sources – and interpreting it and organizing it in such a way that it can easily be reviewed by underwriters – GenAI is able to eliminate much of the time spent chasing and manually entering supporting documentation. It is also able to surface relevant information in a matter of minutes, rather than having underwriting teams pour over reams of paper.
A More Complete View of Risk
This streamlining of the document management and review process is more than just a convenience, though. It also allows for a more complete assessment of the prospective policyholder. Unlike human underwriters and data analysts, who will likely only review a sampling of available data as opposed to reading hundreds of pages of medical records, GenAI is able to read, digest and extract key insights from all available information, providing a more comprehensive view of risk.
With GenAI tools that are commercially available today, underwriters are able to see a much more complete picture of individual risk, based on a wide variety of resources without the need to harass applicants for every bit of data they can find, and in some cases without even taking fluid samples or conducting face-to-face meetings. It may sound surprising, but the level of detail and insight provided by the data alone is often even more robust than anything that could be gathered by drawing a blood sample or conducting a face-to-face interview.
Better Biometrics
All of this opens the door to a new wave of innovation that will push the industry even further into the future by capturing real-time, real-world data directly from prospective insureds. The next frontier of this technology is incorporating biometric data from Apple Watches, FitBits and other wearable devices to get a real-time reading of individual wellness and fitness, which can be used to create highly personalized insurance products.
In some ways, this example is the perfect allegory for understanding the evolution of life insurance underwriting, and how it can become truly hyper-personalized within the next few years. In the old days of life insurance, the actuarial science behind a policy did not go much further than average age and weight and a handful of lifestyle factors. As the industry evolved, we started incorporating supporting data like the existing of a gym membership, and – as things evolved further – scan card data to make sure people were actually going to the gym. Now, we can see their heart rate, average steps per day, blood-oxygen levels and sleep patterns in real-time.
That opens an opportunity for insurers to create truly personalized, highly tailored products that meet the needs of each individual customer and to scale that customization across their entire universe of customers and prospects. That represents a step change in underwriting accuracy and improved customer experience that could redefine the industry.
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