By Neil Sheppard · May 24, 2022
6 minute read

Using data to offer life insurance without a check-up

Big data for life insurance

Life insurance companies are increasingly offering remote medical screenings as a result of the events of recent years. Yet, are medical screenings even necessary? We consider whether advanced data analytics can allow you to offer life insurance without a check-up.

Data is the currency of the 21st century. As our lives are increasingly being lived online, we’re all leaving footprints in the digital landscape, as we do in the real world.

According to Domo, our society creates 2.5 quintillion bytes of data every day. Those traces can be used to learn about us and derive data-driven strategies for optimizing digital services, including life insurance.

Jonathan Phillips, Sales Director Insurance, UK, FintechOS, on life insurance
Jonathan Phillips, Sales Director Insurance, UK, FintechOS, on life insurance

Our data can be used to scientifically determine our risk for life insurance cover:

  • Our bank records can show whether we get our food from a whole foods store or McDonalds
  • Wearable activity trackers show how much exercise and sleep we get
  • Pharmacy records show what medicines we use
  • Medical records, tracked by organizations such as the Medical Information Bureau, show how often we visit a doctor
  • Our employment histories show how much stress we’re likely under at work
  • Our social media can reveal whether we have hobbies and identify warning signs for depression or anxiety

All of this data and much more is already logged in the cloud, It only requires a sophisticated algorithm to merge it all into one profile that can determine the risk factors inherent in our lifestyle. Yet, this new process can go much further.

How much is too much fast food?

We know that a person who lives on fast food, works a stressful city office job, and never exercises is likely to live a shorter life. Meanwhile, an Olympic athlete vegan who lives in the country may live longer. Yet, how much fast food is too much fast food?

Does the harm from a city’s pollution outweigh access to nearby hospitals? Will you live longer on locally produced farm food from the country or supplements and matcha smoothies? Is there such a thing as too much exercise?

When you have data on the lifestyles and medical histories of an entire population, you can start answering these questions scientifically and iteratively. As time progresses, the answers will change with a changing society, and policies can be adjusted accordingly.

This kind of data could tell us that an orthodontist from Munich is at greater risk of diabetes than an artist from Pennsylvania. On the other hand, we could learn that someone who doesn’t drink any alcohol is more likely to die young than someone who has a single glass of red wine once a week.

Using these metrics, a life insurer can determine precisely the level of risk of each customer and offer them a tailored premium for their specific circumstances. All of this can be achieved through automation, without the need for a customer to attend a medical check-up.

Yet, there is concern as to whether this is fair. The Pew Research Center discovered 60% of customers in the USA already assume it’s not possible to go through daily life without data being collected about them. Is it ethical to deny someone life insurance cover due to a data algorithm that’s beyond their control?

The ethics of data-driven life insurance

The ethical question caused by data algorithms is, in fact, a simple misunderstanding of what an algorithm is. In the above study, 59% of Americans admit they’re not clear on what the data that’s being collected on them by companies is used for.

In fact, data algorithms are simply a smarter way to make the decisions that insurers have already been making about customers for hundreds of years.

If a candidate for life insurance is suffering from a terminal illness, then life insurance will likely not be provided. Whether that decision is made on the basis of data or human consideration has no impact on the morality of it. In fact, algorithms are more likely to make accurate, unbiased decisions than humans.

Of course, there is a growing cohort of consumers who are wary of sharing too much of their information. Forbes reported that 69% of consumers are concerned about how their data is collected by mobile apps.

This is why it’s essential for insurers to prove to customers that their data will be used ethically. Primarily, they need to show that it will be used for the purposes that they have agreed to.

Selling data to advertisers is a quick way to make cash, but not a valid long-term strategy to win over customer trust. Not to mention, it may well be illegal.

The key here will be proving there is a purpose and value to providing data to insurers and that doesn’t end with lower premiums for those customers who can show they are living a healthy lifestyle.

Using data, insurers can even form metrics for the optimal lifestyle needed to remain healthy. They can then offer tailored advice to customers on keeping healthy and keeping their premiums down, from more exercise to less meat.

Accurate data for a new kind of life insurance

While data is a concern for consumers, a lack of digital capability is a far greater issue. More than half of patients surveyed by Accenture expected digital services from insurers before the pandemic even began, let alone now.

Source: https://www.accenture.com/us-en/insights/health/todays-consumers-reveal-future-healthcare

Source: Accenture

The answer to this dichotomy is to provide data-driven digital services that customers can trust. That, however, requires algorithms to have accurate data to work with.

The growing pains of the nascent data industry mainly focus on the disparity between data formats in the various sources that we’re pulling from.

As a simple example, one source may hold a customer’s name as [full name], which includes their first and family name in the same field. Another source may list [first name] and [surname] as two different fields. This is a small difference, but it’s enough to disrupt an algorithm working to merge that data into a single field that can be used for analysis and comparison.

Converting that information into a consistent format has been the focus of data scientists in recent years. Yet, there could be another way.

Evolutive approach to data for life insurance risk management

Modern insurtech tools aren’t restricted to consistently labelled fields or tied to a single file format. Evolutive data tools use sophisticated artificial intelligence (AI) to merge and manage data so it can be extracted and used efficiently.

This means raw data can be mined from any legacy system and utilized by advanced AI algorithms. All this without the need to optimize and format them first.

Major players in the insurance industry are already taking advantage of this technology. According to the Willis Towers Watson survey, large portions of the market are already leveraging data to some extent:

  • 70% of large carriers
  • 50% of midsize carriers
  • 54% of small carriers

Clearly, life insurers not taking advantage of Big Data to drive their services face being left behind.

To find out more about how you can offer data-driven, digital life insurance, visit our Northstar platform page.

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