Who Pays For Aging?

The New Timeline of Risk Projection

The impact of longevity on underwriting and mortality standards

by Phil Bruen

My company has been writing and pricing life insurance policies for generations and, as a leader in group life insure in the U.S., covers hundreds of thousands of individuals who purchase additional life coverage beyond their employers’ basic plan. The company employs scores of actuaries to correctly categorize and price each risk presented. Considering that today’s applicant could be covered for decades, how does a company like ours predict what the future will hold, as life spans have gotten ever longer?

 

Lifespans, longevity and health

Insurance has always been about the law of large numbers. Life insurers have used the most accurate data available to them to help underwrite and price insurance policies since probability theory was established in the mid–1600s and the first mortality table published in 1693. By gathering information and best practices, actuaries were able to apply mathematical rigor to determine life expectancy.

Over the course of the 20th century, average life expectancy increased by 30 years, 25 of which are attributable to advances in public health, so insurers have been through this process before. Collected data increased as the century progressed, such as the U.S. government’s official life tables through the National Center for Health Statistics, and actuaries tweaked life tables factoring in both demographics and the personal attributes of applicants, giving insurers more ability to predict risks accurately. By about 1980, life insurers were able to introduce preferred rates for nonsmokers and later began to roll out other types of preferred underwriting.

Old assumptions have gone out the window as lifespans have lengthened, however. U.S. life expectancy at birth increased from 70 years of age in 1960 to 79 in 2015, and for a person who reaches age 65, U.S. life expectancy is now about 84. LIMRA’s well-known research from its Secure Retirement Institute showed a 50 percent chance that at least one member of a 65-year old couple (of average health) will live beyond age 88 and a 25 percent chance that one of them will live to 97. These numbers are game-changers for life insurers, for both insurance and annuities.

Collected data and received knowledge about traditional life spans was based on many factors, including people’s experience with diseases – many of which were incurable or untreatable – and today’s individualized behaviors such as better nutrition, exercise, and medical care don’t appear in life tables. Even former “knockout” conditions such as increased blood pressure, cholesterol or weight do not result in refusal of coverage in many cases. Likewise, many diseases are more manageable today than in the past; 90 percent of people who have a heart attack now survive.

Turning to data

Traditional sources remain rich fonts of information, even as insurers look to take on and price risks that were unanticipated a few generations ago. Life reinsurers, for example, play an increasingly important role to life insurers, not only in providing capital to the sector, but serving as data aggregators who can assist insurers in underwriting more accurately.

But the biggest difference at the current time is the role of third-party data, which is becoming increasingly important as its predictive ability supplements traditional underwriting knowledge. Such data sources, used with applicant consent, provide information on medical history, prescription drug use, financial stability, and likeliness of accident, based on lifestyle as well as demographics. Sources such as the MIB (Medical Information Bureau), motor vehicle reports, and pharmaceutical databases can provide information that helps to predict longevity far better than traditional methods alone.
Lab records are a case in point: with the three major laboratories in the U.S. long collecting data on millions of individuals, they can provide years of many individuals’ medical histories directly to insurers. They have even created scoring mechanisms to provide the sum of that history in a numeric form that can be submitted and analyzed very rapidly.

many diseases are more manageable today than in the past; 90 percent of people who have a heart attack now survive

Lifestyle and other behavioral information can supplement such wellness data. Many vendors now aggregate consumer information for multiple uses from some of the billions of electronic transactions that occur each day. Many of these data will be of little or no use to insurers, of course, but analyses can be run to find patterns of behavior that were not available in the past. As vendors create more and better models based on such variables, insurers may be able to use them to gain a better picture of the true mortality and morbidity risks presented.

There is an important caveat: the quality and source of the information may be questionable. Self-reported data can be biased, and publicly available data are often only available for some individuals and not all. Some searches pull back information that may not match to the intended individual. These are all obstacles that life insurers need to overcome as they begin to incorporate new data sources into underwriting, and part of the reason underwriters and actuaries aren’t going away any time soon.

Implications

Less-detailed underwriting in the past provided insurers with a few categories with a lower match of risk to rate — even the development of preferred rates for nonsmokers is a fairly recent development in this centuries-old industry. Increasing use of medical information and fluids-based underwriting eventually led to standard, preferred and super-preferred ratings classes. But newer data sources are allowing life insurers to better understand the risks they are taking on: will this person life to 70? 80? 90? New rating classes and limits can broaden availability.

Greater personalization; greater accuracy
The holy grail for life insurers would be to match any given individual with their exact lifespan, but there are of course far too many variables for that ever to be achieved. New data sources are, however, adding to underwriting accuracy, including wearables, social self-reporting, and facial recognition. Already in use by some insurers, wearable technology can monitor health conditions such as exercise and rest, in exchange for ongoing coverage or discounts based on healthier living.

Future: An even better match of risk to rate?
In a world where more and more data are gathered every day, insurer dependence on data will only grow in the future. Technology is being developed to monitor adherence to prescription medications, which would be useful information for insurers to know in pricing and underwriting someone with a health condition that formerly would have been a knock-out. Cutting-edge facial recognition technology says it can tell your true age, smoking habits, and many potential health conditions. Will it soon be able to predict how long you will live? And, if the health status is proven to be accurate, will insurers still pay for medical tests in underwriting? Innovative, even disruptive, technologies in medical care will transform healthcare and continue to affect both mortality and morbidity, and additional use of genetic information may become the norm, assuming no further government limitations.

Conclusion
Underwriters in the past had fewer data points to underwrite with. At present, better matching risk to rate allows for more classifications and better rates for many insureds. This can help life insurers write stable and profitable business, even as lifespans increase. In the future, additional medical advances will likely mean even longer lifespans, which the life insurance industry will respond to by appropriately classifying and rating risks. ◊

 

One response to “The New Timeline of Risk Projection”

  1. John Smith says:

    I worry most about that Data Quality issue, how much time does the average underwriter spend verifying the sources? With a common name like mine I could be screwed.

Leave a Reply

Your email address will not be published. Required fields are marked *