Bespoke, Or Not to Be
In the 21st century new technology has continued to shape everyday life. Whether it be open banking apps, fitness watches, or vehicle telematics, individuals are generating masses of personal data each day. This article explores the opportunities for insurance to make use of this data, as well as highlighting the possible pitfalls and hurdles we may face.
Underwriting
Underwriting requires different levels of information gathering depending on which insurance products are being sold. Often a mixture of financial and demographic information is required. In motor insurance we need to understand the value of the vehicle at risk and the risk level of the driver. In life insurance we want to identify an appropriate level of insurance for the insured’s needs. This could be linked to a loan amount, such as mortgage.
There is a balance between collecting too much information and too little. The aim of underwriting it to separate policyholders into homogenous risk groups, whilst maintaining statistical significance. If groups aren’t suitably identified, then the insurer is at risk of not being competitive for standard lives. There is also the risk that they do not offer appropriate premiums for risky individuals, which will lead to losses.
If an insurers underwriting process is too onerous it will put off potential policyholders. Insurers need to be aware of wider market practices, to offer similar amounts of underwriting to competitors. If a insurer was to require copious amounts of health checking (e.g. medical exam) for a policy that has light underwriting from competitors, it is unlikely to attract high volumes of applicants.
A useful article with a more in-depth description can be found here: What Is Insurance Underwriting? (thebalancemoney.com)
Smartwatches and Trackers
Smartwatches have become increasingly popular in recent years. There are a range of use cases, however most of the leading brands focus on some combination of biometric health monitoring, fitness tracking, scheduling, and smart features.
Biometric data includes heart rate, blood oxygen level, blood pressure, and temperature. This information can be used to track cardiovascular and respiratory health. Temperature data can also help track menstrual cycles. This data alone is interesting to the individual; however, it is likely that predictive models could catch early warning signs of conditions/diseases.
Movement information paired with GPS can track walks, hikes, runs, and other forms of workout. This information will indicate how active a lifestyle the individual is living. Paired with heart rate monitoring, an estimation of calories burned can calculated. This information can educate the wearer on their level of activity and encourage them to get the minimum level of movement and exercise in their day.
Smartwatches are relatively accessible compared to other medical monitors and can give access to some features for as little as £70 – though, of course, more premium watches can cost upwards of £400. This figure can be inflated further from premium or exclusive watch straps, bands, and brands.
Due to the accessibility to smart watches, we have seen a steady increase in sales since their popularisation through, notably, Fitbit (launched 2009) and the Apple Watch (launched 2015). Smart watch units reached 127.5million units sold in 2021, compared to 1.39billion smart phone. Infographic: Smartwatch | 2021 | Counterpoint Research
Despite smart watches becoming popular in the recent years, I cannot write this article without honourable mentions to the Pulsar Time Computer Calculator (1976), IBM WatchPad (2000), and the Samsung S9110 watch phone (2009). Each of these examples show that we always wanted more than a timepiece, we just needed technology to catch-up to asipirations: Smartwatch - Wikipedia
Biometric Underwriting
If insurers were to use biometric data, they could reduce the underwriting process for a policyholder making the process more streamlined compared to competitors. The insurer can continuously underwrite the individual as their habits evolve, offering cheaper rates to those policyholders that exhibit healthy behaviours, or traits, throughout the lifetime of the policy. This is a form of incentivising the policyholder to practice healthy behaviours, ultimately reducing the risk of claim.
A healthy individual may be happy to share their biometric and fitness data with an insurer, should it mean that they are able to pay a cheap premium in return. We already see examples of this with this from Vitality (Discovery Group) where you can earn points to reduce your premium by submitting Blood Pressure, and activity information. We are not quite at the point of an insurer intercepting all wearable data to calculate premiums, but surely, it’s a matter of time.
There is a free effect that individuals with access to some form of fitness tracking, be it an Apple Watch, or a smartphone pedometer, will be more aware of their fitness levels, and therefore be able make informed decisions about their health. In recent years we have seen insurers offering discounts on smart watches and gym memberships for this exact reason. The circular argument is that individuals that are more aware of how their lifestyle affects their health, are more likely to purchase and own health tracking items such as smartwatches.
Risk Pooling and Ethical Dilemmas
The purpose of insurance is to pool risk and replace low probability events of high cost, with known regular payments. The insurer purchases the risk from the individual and pools together similar risk groups, diversifying the risk through high volumes of policies sold, allowing for claims experience to be analysed through mathematical and statistical methods.
If we get to the point that we know every heartbeat, every breath, and every step of an individual, then surely, we can encourage healthy behaviours, reduce risk associated with writing policies, and write a bespoke insurance policy for each individual so they pay for their own level of risk.
Unfortunately, this leads to the question, if we are able to better identify individual risks, do we charge an appropriate or accurate premium for that individual’s risk, or do we continue to pool risk? The healthiest appearing individuals would benefit most from individual bespoke premium pricing, and those that appear unhealthy in the eyes of the insurer would need to weigh the risk of being uninsured against a high premium.
It is clearly competitive for companies to use as much information as possible to offer the cheapest rates to healthier individuals, however is it ethical if we outprice the people that in theory need the insurance more? It is quite possible that this is something that will need to be considered in future regulations to encourage a minimum amount of pooling, or a maximum percentage difference between the lowest and highest premiums offered, and consideration around what grounds an insurer can reject an application.
Another concern is the access to biometric information and how the policies leverage these metrics. I believe that three of the most important outstanding queries are as follows:
· Policyholders should have the choice to not share this sensitive information i.e. it should be on a voluntary basis.
· There needs to be a high level of security from firms that store and use this type of information.
· The insurer should not be able to coerce or discriminate against a policyholder that does not seek medical diagnosis, even if their model indicates that the individual is at risk.
I certainly can’t predict the future, but maybe my watch will be able to one day. For now, we will live in the present and watch how insurers react to this new data source.