By Farooq Ahmed Farid | 11 Aug 2021

How do health insurers assess risk?

Several factors go into calculating your health insurance premium, with most of them associated with risk. But how do insurers assess risk accurately to ensure you’re paying the right price for your private health plan?


Most people have a basic understanding of how insurance products work. The more likely the insurance company, or the insurance underwriters, feel they will have to pay out against your policy, the more expensive your premium will be.

While several factors influence the price of your health plan, risk remains the most significant. For example, suppose you're in your 80s and have a history of recent ill-health. In that case, the risk associated with providing you cover is unlikely to be mitigated too much by you choosing a higher deductible!

How health insurers assess risk is changing thanks to advancements in technology. How is this happening, and are all the changes for the better?

Collective vs individual risk

One of the most discussed topics in the insurance sector for many years has been whether risk is managed collectively or individually.

Most people think they get a premium tailored to them. And why wouldn't you, given all the details you must provide? However, this isn't always the case. At least, there isn't, or wasn't, always as much of a difference in premiums as you might think between low risk and high-risk individuals. This worked in much the same way that a bank may offer customers with vastly different credit scores the same rate for a loan.

But things are changing, with a shift towards using more sophisticated pricing models to ensure people get a premium that better reflects their circumstances.

The key is in the data!

We must recognise that the old way of doing things wasn’t actually insurance companies and underwriters doing things differently.

Health insurers have always looked to price premiums as accurately and fairly as possible, based on the data and techniques available at a specific time. As such, what many people thought as "collective pricing" was the best insurers could come up with due to a lack of granular information available about specific individuals, as well as data scarcity in general.

As insurers learn more about us and have more data, risk assessing will become more focused and specific to our circumstances.

Using health data, and maybe even genetics in the future!

When you generate a quote for a health plan, you must tell your insurer about things like your weight, exercise levels, whether you smoke, and your alcohol consumption. These are all common questions, although it may be tempting to understate your alcohol consumption and whether you smoke and hope you don't need to claim for something linked to either of these lifestyle factors.

The same is true for asking questions about your family history of heart conditions or certain types of cancer.

In the coming years, insurers will likely increasingly move towards a model based on actual health indicators and screening. For example, many health conditions – and an idea of our general health - can already be identified and even forewarned through blood testing. So, health insurers could potentially request that potential and existing members undergo blood mapping before getting a new or renewal quote. Such an approach would ease the burden on individuals to undertake a lengthy application process and help insurers and underwriters make decisions based on factual, accurate data.

Another, perhaps more controversial, possibility is the potential for insurers to use genetic markers and information to price health plans. It isn't beyond the realm of possibility that we could all have our genomes and complete genetic sequences stored in the cloud one day. All we would need to do then is permit access to our data to generate a health insurance quote.

The use of big data for bigger picture considerations and trend identification

What is already increasingly prevalent in health insurance pricing is artificial intelligence (AI) and machine learning, which insurers use to generate premiums.

The use of this tech will only grow, with "Big Data" meaning insurers and underwriters have access to more granular health, economic, societal, and behavioural data than ever before.

The prominent role of AI and machine learning will be to find trends and correlations in data. For example, if data identifies many people who require treatment for condition A later develop condition B, insurers and underwriters can adjust premiums to reflect this. They can also consider a vast range of additional factors. Such an approach would personalise health plan risk assessments like never before and ensure that as many people as possible get a quote reflecting their circumstances.

What will the consequences of a change in assessing risk be?

Think back to what we said earlier about collective vs individual pricing.

The more health insurers know about us as individuals, aligned with what they can learn about bigger picture trends from data, the more relevant our premiums will be. Therefore, the most likely outcome is that premiums for individuals at the lower end of the risk spectrum will reduce while increasing for those who present a higher risk.

Of course, there are significant differences already, but these could widen quite considerably. In the long-term, governments and health bodies may raise questions around regulation if it makes health insurance (and other types of insurance) unaffordable to those who need it most. For example, would society accept older people living in relative poverty because of the price of a health plan? In addition, we might also consider that people typically take out life insurance over a very long-term period. Could approaching health insurance like life insurance be an option where people pay more when they're younger to offset potential costs as they age?

Is pricing via risk fair, and how can insurers mitigate if not?

Most people would probably agree that pricing via risk is fair. After all, if you're at a low risk of developing a health condition, it's reasonable to question why your health insurance premium should subsidise those at a higher risk.

However, if genetics and "Big Data" play a role in the future of risk assessments and health plan pricing, sometimes the risk we present will be beyond our control. One solution may be that factors like age and genetics, while still being used in risk assessment, become less important, and lifestyle factors and how we choose to look after ourselves become the dominant drivers of health plan pricing.

One outcome of this is that there will be clear incentives for people to manage their health and lifestyle proactively. You can't control when you move from being under 50 to over 50, for example, but you can choose what you eat, how much exercise you take, and how you manage your overall health and well-being.