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Earlier this year, the Society of Actuaries published a report called “Health Expectancy,” which explored specific population cohorts by age, health condition, gender, and other factors. “Health expectancy” was defined as the number of years specific population groups are likely to stay healthy and independent, as opposed to time they can be expected to spend, say, in assisted-living facilities. Among the society’s pieces of advice to life-span modelers: A healthy 75-year-old man can expect to make it another 10.52 years without needing assisted living or full-time nursing care. But a diabetic 75-year-old man can only expect 7.92 more healthy years. A 75-year-old woman who has osteoporosis but has suffered no fractures and doesn’t have diabetes can enjoy an additional 8.16 good years.

The quest to perfect life-span modeling could ultimately benefit various industries in addition to insurance and health care. Organizations that maintain pension funds or offer lifelong health benefits have been struggling to keep pace with the expanding population of elderly retirees. General Motors, for example, disclosed in the spring that its pension fund is underfunded by $27 billion. Insurance companies commonly assume that death rates will decline 1 percent a year. But if their forecasts are off by as little as 0.25 percent, they can lose billions across many funds at a time of severe economic distress, says Olshansky.

There are now several artificial-intelligence programs that can model individuals’ longevity on the basis of their health status, pharmacy records, lifestyle, and habits—and then predict the potential impact of behavioral changes. Some health plans are using this data to target members who face an increased risk of developing chronic diseases because of lifestyle liabilities such as obesity and smoking. “They can tell specific members ‘If you change your lifestyle, we’ll lower your premium,’” says Anand Rao, a principal with PricewaterhouseCoopers’ Diamond Advisory Services unit.

Fine-tuned life-span models also benefit the growing market for “life settlements”—life-insurance policies that investors purchase from individuals. “That investor makes a bet on how much longer you have to live, pays the premium until you die, and then gets the full amount of the policy,” explains Robin Willi, owner of Rigi Capital Partners, a Swiss company that purchases and manages life settlements. They’ve become popular investments because they produce yields of 12 to 20 percent, making them more attractive than bonds, which yield as little as 4 percent, Willi says. But investing in them is a fine art. The ability to make a good return, Willi says, “depends on old people dying in a predictable manner.”

So life-settlement investors make assumptions about life span based on data they collect on past holders of life-insurance policies. For example, such investors assume that an upper-middle-class person who has a $5 million policy and resides in Florida is likely to live longer than a Midwesterner with a $500,000 policy. “If you can afford a $5 million policy, and you can afford to live in a gentle climate, then you can also afford good medical care,” Willi says. “It sounds cruel, but it’s just reality.”

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Credit: Technology Review

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

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