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For most of the past century, the financial-services industry has used actuarial tables to design life-insurance policies, pensions, and other products based on predictions of human life span. These life tables” rely on historical death rates to predict the future longevity of broadly defined population groups. But human life expectancy has increased dramatically—from 47 years in 1900 to 77 today in the United States, with similar surges around the world. That’s led to skyrocketing pension and health-care costs. What’s more, sizable variations in longevity have emerged among different subgroups. Thus the financial-services industry no longer considers life tables adequate, as they leave too much room for companies to lose money.

A growing number of corporations and governments are turning to an emerging group of lifespan modelers. These experts are studying the living in an attempt to predict who will make it well into old age—and who won’t. “Life tables are crude and based on the past,” says S. Jay Olshansky, a professor of epidemiology at the University of Illinois at Chicago and cofounder of GD Analytics, a longevity consulting firm. Olshansky says we now need to “generate much more finely grained estimates of survival.”

A couple of years ago, Olshansky traveled around the world lecturing J.P. Morgan Chase executives on the rapidly changing face of human longevity. It was an eye-opening lesson for many at the firm. “He had striking visuals showing the growth of obesity in the Western world,” recalls Guy Coughlan, a managing director in J.P. Morgan’s London office. “He showed us how individual trends specific to a country, social class, or occupation all play into longevity.”

For example, Americans between the ages of 55 and 64 are not as healthy as the generation that preceded them into retirement, Olshansky says. People born in the United Kingdom in the 1930s—often called the “golden cohort”—have lower death rates than the generations that came before and after them. Insights like these are vital to J.P. Morgan, which is among a growing number of Wall Street firms that offer “longevity swaps”—insurance policies that pension funds use to hedge the risk that pensioners will outlive their funds’ reserves.

In addition to correlating health conditions with longevity, life-span modelers drill far deeper into individual traits than traditional actuaries do. So, says Olshansky, he’s not just a 56-year-old white male in the United States. “I’m also a Jewish male with a given level of education,” he says. “Education and religious background actually play an important role, as do the state you live in and the duration of your parents’ lives.”

Other companies are creating models based on what people buy online, what magazines they read, and even what hobbies they pursue. According to a recent Wall Street Journal report, Deloitte Consulting uses models that predict individuals’ risk of specific illnesses on the basis of their exercise habits, shopping patterns, and hankering for fast food. Future models may even include genetic test results and data from wireless health monitors. Scrutinizing all these factors yields insights about specific subgroups of the population, which financial professionals can then use to predict the longevity of individuals and the average longevity of groups.

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Credit: Technology Review

Tagged: Business, Business Impact, aging, Predictive Modeling, lifespan

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