Do giant meteors even matter?
How powerful external forces affect our ability to identify and understand risk?
Gailynn Nicks, Chief Product Officer
6 mins
Your heart is pounding, palms clammy, tension spreading through every muscle in your body. You are rooted to the spot not wanting to scare it. Not scare it, you know you won’t scare it. But you don’t want it to move again. It twitches. You jump out of your skin, but somehow don’t move a muscle. Gently, slowly, so slowly, but not too slowly, you lower the cup down making a small glass IKEA cage for this otherwise well-meaning house spider. Your entire body releases with calm.
All imagery AI generated.
What are you afraid of? You may love spiders (statistically unlikely, but you may) Snakes? Wasps? Heights? Open spaces? Crowded spaces? Small spaces? What would you do if they all went away? What would you do if you weren’t afraid? The well-known self-help book “Feel the Fear and Do it Anyway” addresses the need for us to relabel what we think of as anxiety or fear as excitement or anticipation, but when we experience it, fear is real, and fear is personal.
Spiders are one thing, but we make many of our decisions about danger and risk when there is no immediate threat. If we look at the bigger picture, how we perceive danger and how we assess, manage and mitigate it is both an individual and a team sport.
In defining a risk, we think about what might happen and what might matter. Aven and Renn (2009) offer this definition “A risk is a situation or event where something of human value is at stake and where the outcome is uncertain”. Both parts of this definition ask for judgement, a weighing of things.
In an objective way we might ask:
But are the answers to any of those questions objective? It’s easy to think that assessing risk is somehow an objective task, but Cultural Theory proposes that our perception of risk is a social process, whereby some risks are recognised whilst others suppressed depending on one’s values and worldview. Anthropologist Mary Douglas said, “Society defines itself by how it defines and manages dangers. Societies fragment when they see dangers too differently”. We have recently seen this in practice globally during the Covid pandemic and in the UK with Brexit.
Differences in how dangers and risk are framed and perceived is strongly influenced by deeply held values and beliefs. People are polarised not because they cannot be objective, but because their individual objectivity is based on unprovable assumptions about likely outcomes, formed through the lens of these values and beliefs. Even as outcomes unfold, there is little to suggest that any prior assumptions can be definitively proven to be absolutely accurate, illustrating the degree to which understanding people in context, is fundamental to understanding risk management.
Which risks matter? And which should we try to mitigate? Some hazardous events could be apocalyptic. A giant meteor might come along and wipe out our planet – would that matter? Perhaps not. Regardless of the degree to which we did or did not “believe”, if we all die instantly it makes no difference, but that is seldom the case. What about a pandemic? If we ignore a danger or a risk, it doesn’t change its likelihood of happening. However, it often means we don’t think we can, need, or afford to plan for it. What the pandemic and other recent events illustrate is the extent to which our prior beliefs, our trust in those conveying the information, and our options in the face of it influence our perceptions of how much danger we face.
What are the implications for how people perceive and price the transfer of risk?
How we both define and measure things has an enormous influence on how we gather and present the data in the first place; how we use that data to frame an argument even more so (how do we define, measure and present “COVID deaths”?). This is one of three main areas where insurance affects how people manage their own risk. Insurers use historical claims data to allocate “objective” measures to the likelihood of a bad outcomes, but data also lies. In mature sectors, like car insurance, there is a greater common understanding, through shared experience, of what the risks are, how humans deal with them, and how that risk can be transferred at a price. But emerging areas such as cyber security, and changes in absolute risk levels with increased incidents of flooding and other environmental events, has implications for risk pricing - both at a societal and an individual level. Understanding how individuals or groups of individuals value security and view danger creates new opportunities to insure at a rate that represents a fair exchange of value. Embedded insurance is one way that we identify some of those opportunities.
The second key area is in what is insurable. The range of risks we take is far broader than those that those that can be insured. There are many kinds of risk we take in all aspects of life, not all insurable. As the late Queen said, “Grief is the price we pay for love”. In some research we conducted, we found that many defining themselves as risk-takers excluded any emotional risk from that definition, however many defining themselves as risk-avoiders were more prepared to take a friend’s car keys at a pub if they had been drinking, or to trust the feedback from their loved ones. In fact, when people self-defined as risk-takers, what they meant was that they thought they could “outsmart” risk through their superior knowledge or ability. In other words, they felt they had the ability to take more control of their risks through accurately assessing and mitigating them.
Unsurprisingly, self-defined risk taking is strongly correlated with age, whereas risk outcomes are far more evenly spread. So, we each have risks we think we can manage and mitigate and some we don’t. The emerging IoT data being used in health and motor insurance is one way insurers are identifying ways to encourage mitigation of risk at individual levels, whilst still maintaining the values of pooled risk.
Lastly, where understanding people and their individual risk frameworks makes a difference, is in dealing ethically with the changes to information asymmetry being brought about by insurer access to big data. Current approaches to the use of big data by insurers tends to disadvantage consumers from a price and cover perspective in ways that are proving to be against regulation, even when this is unintentional. But the use of greater data symmetry can also be used to make people more aware of actions they take now and in future, that might affect their ability to use their insurance as intended. We already have deep understanding of the types of bias in play that are based on data asymmetry.
Adverse selection comes into play before a contract is in place. It means we may choose to insure things where we believe we may be more at risk than the average - if we are careless with our mobile phone we may be more likely to insure it than if we are not. Moral hazard comes into play after a contract is signed. It means we can be incentivised into bad or exploitative behaviours we wouldn’t if we weren’t covered – we may ski backwards down a mogul run if we know our medical bills are covered. It may also mean we forget to lock our doors or turn off the iron before we left for the airport in the first place.
Starting with the person means a better way to make transparent how claims and premiums are affected for the individual and the group by creating clearer rules that can be enforced. As they say, “good fences make good neighbours”.
Using personal risk frameworks within the context of insurance is one way to manage the pricing of risk transfer in new or changing areas of insurance. It is also a good way to balance regulatory requirements with the need for ethical approaches to big data – it enables insurers to make business decisions that lead to better margins through a fair exchange of value.
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