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In a world full of data and advanced technology, we are able to make a more accurate assessment of risks at an individual level, and that has put serious pressure on the principle of solidarity.
Smart use of data leads to enhanced predictive capabilities
In the future, it is quite possible that your personal lifestyle will be a decisive factor for the calculation of, for example, the premiums you pay for your car insurance. This could be good news for people who play a round of golf at the local golf club every Sunday. In general, people in this group lead a less risky lifestyle than people who go to the local football stadium every Sunday to watch a football match. So perhaps in the future playing golf will be a data category that insurers could use to offer lower insurance premiums, because why should people who like to play golf have to ‘pay’ for people who like to watch football?
However, it isn't quite so straightforward in practice. After all, leisure activities are just one of the many factors that say something about (the risks of) an individual. Only by combining all the different sources of information can we say something about individual risks with a certain degree of probability. That is essentially why Big Data has become so important in recent years. It basically comes down to one thing: data makes it possible to make much more accurate predictions.
This increased level of predictability has fundamental consequences for insurers: it puts the principle of solidarity into an entirely new perspective. The reasoning behind this new perspective is quite simple. Because data is available about every aspect of our lives, we know more about the individual risks we face in terms of becoming ill, having a (workplace) accident, or getting into financial or legal problems. We thus also have a better picture of how our individual risks compare to the average of the general population. In the past, we only had a rough picture of an individual's situation, and an insurer had to calculate the insurance premiums purely on the basis general information. Now it can be done with surgical precision.
The age-old concept of solidarity, which has been the foundation of all insurance undertakings, is therefore not quite as rock-solid as it used to be. Or, to put it another way, the new insights provided by data will test exactly how much solidarity we really want to have with each other.
How willing are people to keep on sharing risks with a group of people that they know – now they have the data that clearly shows it – have much higher risks? This is also known as the solidarity paradox: the more we know about the way we ‘subsidise’ other people who have higher risks, the less willing we are to do so. Especially when we know exactly how much we subsidise them: data analysis techniques make it clear that falling ill isn't just bad luck in many cases, and that there is a direct correlation between insurance claims and individual behaviour.
Opportunities for insurers
Insurers have been responding to these developments for years. In some countries, for example, it is almost impossible for young people to get car insurance simply because the data shows the risks for this category are so high. It is also why in some countries the use of telematics in insurance – for the monitoring of driving behaviour – is becoming increasingly popular. The problem of taxi drivers getting insurance is another example of how developments in society are changing the insurance landscape.
If taken to the limit, insurers will be able to customise all products based on your personal lifestyle: the three sensors in your smartphone (GPS, accelerator, and gyroscope), for example, offer countless possibilities. Insurers not only know how far you cycle on your bike, or how often you eat out at a restaurant, they can even see if you are a driver or a passenger in a car. That unlocks an enormous potential for customisation. It is only a question of how willing people are to share this amount of sensitive personal information.
Are personalised insurance policies the solution?
Personalised premiums appear to be an ideal concept. It is definitely a subject that is often highlighted during panel discussions. There is a danger, however, of taking it too far. In 2018, the introduction of a controversial social credit system in China made the headlines.
Every citizen is awarded plus points for good behaviour and minus points for bad behaviour. For example, you get plus points when you visit your parents or grandparents, but minus points when you buy alcohol. The final score of all these points is your social credit rating - or how “trustworthy” you are - which can play a decisive role when you apply for a loan or insurance. This is the most extreme form of product personalisation.
The system is based on the assumption that behaviour can be perfectly modelled, and that we can accurately award plus points or minus points for behaviour that deviates from the norm in a positive or negative way. This hypothesis might work in a laboratory-type setting, but in real life the situation is a lot more complicated.
Take, for example, the following situation: do you get plus points or minus points for going to a gym every week? Do a survey of an average old people’s home, and there is a big chance that many of the residents have never even been inside a modern gym. Conversely, there are plenty of (professional) athletes who die at a much too early age. So which group should be entitled to lower health insurance premiums?
Going to a gym can be good for your health, but just because you go to a gym it doesn't mean you're healthy. And what if somebody has a very healthy lifestyle and goes to the gym every week, but then they also go on holiday to a tropical country every year, which means they have a much higher risk of catching a tropical disease? How will this affect the amount of health insurance they have to pay?
When you think of how complex the world really is, the concept of solidarity actually has a lot of advantages. Everyone is unique, and that is difficult to model. If somebody makes a conscious choice to do something with a high risk of accident or injury, then it is logical that they should have to pay a higher premium for this. And if we can quantify these risks more accurately with IoT and data, then that makes it even better. After all, it would help to create a more transparent and fairer system of insurance. On the downside, however, there is the danger that some insurance companies will no longer be willing to accept ‘high-risk groups’.
Even though the principle of solidarity is admirable in a general sense (all the insured consumers carry the risks collectively, irrespective of how high their own risks are), the advanced technology we now have at our disposal could cause a fundamental shift in this area.