For centuries, humans have used life insurance tables to estimate their life expectancy. Artificial intelligence is now tackling that challenge, and the answers may be of interest to economists and wealth managers.
Death Clock, a recently released AI-powered longevity app, has become a hit among paying customers and has been downloaded about 125,000 times since its release in July, according to market intelligence firm Sensor Tower.
The AI was trained on a dataset of more than 1,200 life expectancy studies with approximately 53 million participants. Predict your date of death using information about your diet, exercise, stress levels, and sleep. The results are a “pretty significant” improvement over standard life table predictions, said developer Brent Franson.
Despite its somewhat morbid undertones, including a “happy farewell” Day of the Dead card featuring the Grim Reaper, Death Clock has become popular among people trying to live healthier lives. Masu. The app ranks highly in the health and fitness category. But the technology could have broader applications.
Life expectancy is key to all kinds of financial and economic calculations by governments, businesses, and individuals, from retirement income needs to life insurance and pension fund coverage to financial planning.
In the United States, where life expectancy has fallen behind other developed countries in recent years, the Social Security Administration has its own mortality table, which is published in annual financial reports to trustees. .
Government agencies currently predict that an 85-year-old man in the United States has a 10% chance of dying within a year and has a life expectancy of 5.6 years. But such averages can have large margins of error, Franson said, and the new algorithm could provide a more customized reading – a customized death clock.
The interest of such findings in economics is evidenced by the publication in the past month of two papers on the subject by the National Bureau of Economic Research.
“Take advantage of the benefits”
One of these, entitled “On the Limits of Chronological Age,” considers the various effects that the aging process has on physiological performance. The study found that although policies such as statutory retirement are typically based on chronological age, many aspects of economic behavior, such as readiness to enter the workforce, are not well captured by people’s chronological age. It turns out it’s possible.
Researchers at Harvard University and London Business School say that continuing to rely on chronological age as an indicator of how well people can function could prevent society from “taking full advantage of the benefits of longer lifespans.” It is concluded that there is.
Another research paper looked at “value per statistical life” or VSL. VSL is an insensitive measure used for cost-benefit analysis in areas such as pollution control and workers’ compensation. It is usually calculated based on the compensation of workers in high-risk jobs.
The researchers behind the NBER study, “The Statistical Value of Life for Older People,” tapped into another data set: the propensity of older Americans to spend money on health care services that reduce their risk of death. They found that the average VSL for a 67-year-old who reported their health as “good” was just under $2 million, compared to the average VSL for those in “good” health of $600,000. I discovered that.
When it comes to personal finances, better measurements of life expectancy will have a big impact on retirement savings, said Ryan Zabrowski, a financial planner at investment advisory firm Clilogy.
“A big concern for older people who are retired is living longer than their money,” Zabrowski said. He touches on this issue in his forthcoming book, Time Ahead.
“Out the Window”
Decisions like how much to save and how quickly to withdraw assets are often based on rough and unreliable life expectancy averages. AI-driven testing that can potentially reduce that uncertainty is almost unheard of today, but may become a less unusual idea in the future.
Moreover, AI technologies associated with medical advances themselves have the potential to increase life expectancy, with the associated risk of depleting savings. Zabrowski believes the results are clear. As retirements get longer, savers will need higher-return investments for retirement and will allocate more stocks than bonds.
“Traditional methods of measuring demand for stocks will be thrown out the window,” he writes in a recent book. As people begin to expect to live longer, “the demand for stocks will increase significantly.”
There is already a lot of technology out there, such as heart rate monitors and wearable max oxygen consumption meters, that, when coupled with new AI-powered devices, could help reduce uncertainty about an individual’s mortality rate.
Of course, there are always limits. There are a lot of intangible assets in addition to totally unpredictable variables like accidents and pandemics.
longevity disparity
For example, loneliness is often thought to shorten life expectancy. Gratitude may enhance it. A Harvard University study found that women who reported the most gratitude had a 9% lower risk of dying within three years than those who reported the least.
Next is the issue of inequality. Money is important for longevity. Multiple studies, including Nobel Prize-winning economist Angus Deaton’s work on “deaths of despair,” have found stark disparities between rich and poor Americans.
According to a study published by the American Medical Association, the difference in longevity between the richest 1% and the poorest 1% at age 40 was nearly 15 years for men and 10 years for women.
For Death Clock users, who must pay a $40 annual subscription fee, the app counts down their estimated time remaining every second and suggests lifestyle changes that can reduce mortality.
“There is probably no day in your life more important than the day you die,” Franson says.
