For centuries, humans have used actuarial tables to find out how long they are likely to live. Now artificial intelligence is doing the work – and the answers may be of interest to economists and money managers.
Recently released Death Clock, an AI-powered longevity app, has proven a hit among paying customers – it has been downloaded nearly 125,000 times since its launch 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. It uses information about diet, exercise, stress levels and sleep to predict the likely date of death. Its developer, Brent Franson, says the results are “pretty significant” improvements over standard life-table expectations.
Despite its somewhat morbid tone – it displays a “fond farewell” death-day card featuring the Grim Reaper – Death Clock is becoming popular among people trying to live more healthy. It ranks high among apps in the health and fitness category. But the technology has potentially broader uses.
Life expectancy is key to all types of financial and economic calculations by governments, companies and individuals – from retirement income requirements, to policy coverage and financial planning in life insurance and pension funds.
In the US – which has lagged other developed economies in recent years in the life expectancy of its citizens – the Social Security Administration has its own tables for mortality rates, which are included in the annual financial report to the trustees.
The government agency currently estimates that an 85-year-old in the US has a 10% chance of dying within a year, and is expected to live an average of 5.6 years. But such averages may be underestimates by a large margin, Franson says, and new algorithms may provide a more tailored measurement — an optimized death clock.
That such findings are of interest to economics is demonstrated by the publication of two papers on the subject by the National Bureau of Economic Research—in the past month or so.
‘use the benefits’
One of them, titled “On the Limits of Chronological Age”, looks at the various ways that the aging process affects physical abilities. It finds that many aspects of economic behaviour, such as readiness to join the labor force, may not be well captured by people’s calendar age – even though policies such as statutory retirement are typically based on it.
Researchers at Harvard and London Business School have concluded that by continuing to rely on chronological age as a proxy for how well people can work, society is “failing to fully utilize the benefits of increased longevity.” may fail”.
Another working paper examined “value per statistical life” or VSL – a harsh-sounding measure used for cost-benefit analysis in areas such as regulation of pollution or compensation for workplace accidents. It is typically calculated based on workers’ compensation in high-risk jobs.
The researchers behind the NBER study “Statistical Value of Life for Seniors” drew on a different dataset: the propensity of older Americans to spend money on medical services that reduce mortality risk. They found that the average VSL at age 67 for people who described their health as “excellent” is less than $2 million, compared to $600,000 for those in “good” health.
When it comes to personal finances, better measures of life expectancy will have a profound impact on people saving for retirement, according to Ryan Zabrowski, a financial planner at investment advisory firm Creology.
“A big concern for older people, our retirees, is running out of their money,” says Zabrowski, who touches on the issue in his soon-to-be-released book, “Time Ahead.”
‘out the window’
Decisions like how much to save and how quickly to withdraw assets are often based on broad-brush and unreliable averages of life expectancy. AI-powered tests that could potentially reduce that uncertainty are largely unheard of now, but it won’t be such an unusual idea in the future.
Furthermore, advances in medicine as well as AI technology have the potential to boost life expectancy – and with it comes the risk of wiping out those savings. Zabrowski believes one outcome is clear: Longer retirements will mean savers will need higher-return investments for their old age, which will lead them to allocate more stocks to fixed-income securities.
“The traditional method of measuring equity demand will be thrown out the window,” he writes in his upcoming book. As people expect to live longer, there will be a “huge increase in demand for equities”.
There are plenty of technologies already in place – like heart rate monitors and maximum oxygen-consumption gauges from wearable devices – that have the potential, combined with new AI-powered devices, to reduce uncertainty around individual mortality.
Of course, there will always be limits. Besides completely unpredictable variables like accidents or even pandemics, there are also a lot of intangibles.
longevity interval
For example, loneliness is often thought to reduce life expectancy. Gratitude can enhance it. A Harvard study found that women who felt most grateful had a 9% lower risk of dying within three years than those who felt least.
Then there is the question of inequality. Money matters for life expectancy. Several studies – including Nobel Prize-winning economist Angus Deaton’s work on “deaths of despair” – have found a stark gap between rich and poor Americans.
Research published by the American Medical Association found that the difference in longevity between the richest and poorest 1% at age 40 was about 15 years for men and 10 years for women.
For Death Clock users, who have to pay $40 a year to subscribe, the app suggests lifestyle changes that could stave off mortality — as well as a second-by-second countdown of the estimated time remaining Does.
“There is probably no date in your life more important than the day you are going to die,” says Franson.
