Need another reason to obsess over your credit score? Here’s some (if macabre) news: According to a team of economists, this three-digit number isn’t just a good indicator of your stability as a borrower, it can also predict how long you’ll live.
Researchers from the University of California at Irvine and the University of Geneva recently analyzed Experian credit data from 2004 to 2016 for over 2 million randomly selected people. If an individual died during this period, their death was recorded by Experian, allowing researchers to create a computer algorithm that determined whether elements of their credit report – such as a sudden and significant change in credit score – were related to mortality. According to the researchers’ findings, published in a December 2021 paper, this was absolutely the case.
“People behave differently in the credit market depending on specific underlying characteristics,” says Giacomo De Giorgi, a professor at the Geneva University of Economics and Management and one of the study’s authors. “In this case, the risk of death is part of it.”
How credit scores are linked to death
The link between mortality and credit scores manifests itself in two main ways, according to the researchers.
The first concerns how people spend money throughout their lives, especially at the end of their lives. Someone who has just been diagnosed with cancer, on the other hand, will likely spend markedly differently than they did a few months ago. He could rack up medical debt or, if the diagnosis is terminal, max out his credit cards on Mediterranean cruises, BASE-jumping gear, or other expensive, one-time expenses.
The second connection is less direct.
Previous research tells us that people who experience economic stress, such as sudden job loss, are more likely to go into debt, lose access to health care resources, and experience mental health crises through compared to those in paid employment. All of this leads to financial constraints that can shorten a person’s life in the long run. This also shows up in credit data, according to the researchers.
Having a low credit score doesn’t mean you’re more likely to die young — at least not by this metric. (Remember, the paper examines pronounced changes in credit reports, it does not compare individuals’ credit scores.) And since the poorest Americans have no credit, they are not represented in the data.
These new findings also can’t predict exactly when you’ll die based on a specific numerical change in your credit score – the researchers can’t, in other words, say that a 40-point drop means the death is imminent. What this data CAN do, however, is identify such a drop in a person or agency that could organize a life-changing intervention.
Back to this imaginary cancer patient. Let’s say he survives the illness but needs to take unpaid leave to recover. The bills are piling up in the meantime, and he has to apply for a new credit card and max out his spending limit to stay afloat. As a result, his credit rating drops drastically, and due to the high cost of chemotherapy and recovery, it remains low.
In situations like this, the researchers hypothesize that the data could help a company develop services that flag our imaginary patient as a candidate for targeted financial assistance or a subsidized health insurance plan like Medicaid.
“Many people often don’t understand the risk they are exposing themselves to through various forms of borrowing, and how it all leads to increasingly catastrophic outcomes for them,” says Matthew Harding, a professor in the economics department. from the University of California Irvine and the co-author of the study. “People’s lives could potentially be improved by having advanced information about [these] things.”
There is another, more dystopian implication to these findings, Harding says.
If a team of economics professors can predict your health based on your credit score, so can insurance companies. An industry that has a bad reputation for using big data to raise prices could potentially make things like health care and life insurance even less affordable than they already are. Employers could also, in theory, use this information to discriminate against employees.
“Using data to predict outcomes that would otherwise be unattainable has tremendous power,” he says. “But there are also potential ways in which some companies might want to take advantage of it.”
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