Insurance tech: How Telematics and Fitness Devices Are Changing The Industry

“If you want something new, you have to stop doing something old”

― Peter Drucker

Innovation in technology has drastically changed the world we live in within just a few decades, and this has led to noticeable developments in many industries, especially in the insurance sector. More recently, insurers have moved towards offering more personalized insurance by leveraging telematics and fitness devices to price premiums with more accuracy. But is this change truly beneficial?

Background

If you were to purchase an insurance policy today, the numbers you, the policyholder would care about are the deductible and premium. The deductible is the amount of the claim that the policyholder pays if an accident occurs, and the premium (which is based on characteristics specific to you) is the amount of money paid for an insurance policy. The insurance company then pools all the premiums from its customers and uses that money to pay for the losses of those who make claims that year.

If most of the premium is based on characteristics personal to you, it begs the question, how do insurance companies know how much to charge for the factors that determine the premium? For insurers, the pricing algorithm is determined by the following formula:

premium = base rate * rating factor1*rating factor2*…rating factor n + E

In other words, the premium is determined by a base rate set by the insurer, and then the product of rating factors that are specific to the individual. Factors like age, annual distance driven in the case of car insurance, or medical history in the case of life insurance will change the premium. Finally, any other expenses such as a policy fee or increased coverage will be added to get a final amount. For our purposes the part of the formula that is most important to understand is how the values for the rating factors are created. One common method is to use the pure premium approach. Pure premium (also known as loss cost) measures the average loss over a certain level of risk :

It basically tells us the portion of an expected cost of a claim that is solely due to loss. The formula to find the value of a rating factor from the pure premium is as follows:

Although it looks fancy, the formula is very simple; L+EL represents the pure premium, V = variable expense provision (a value we don’t need to worry about right now), and QT= the target profit percentage that the insurance company wants to meet (Werner and Modlin, 2016).

The rating factor values will then change based on characteristics specific to the individual. To illustrate, if we wanted to find the value of the rating factor for age, past historical data would show different pure premium amounts for different age groups. The different pure premium amounts for each age group will therefore change the value of the rating factor, since the numerator of the equation is going to be different in each case.

So, what’s the issue?

We know that premiums are priced based on past historical data and assumptions, but is that process efficient and fair? Focusing on auto insurance, let’s assume that you have only been driving for 2 years but are a careful driver. Your premium will obviously be much lower than if you just received your driver’s license last month; but how much your premium decreases by is still determined by the historical data gathered by the insurance company.

Transformation of Premium Pricing

As a result of the problem mentioned above, there has been a significant increase in the use of telematics and Usage Based Insurance. Telematics is a general term for the use of a device that monitors and tracks vehicles. Usage Based Insurance (otherwise known as UBI) personalizes auto insurance by leveraging historical data, but not data collected from the insurance company’s customers. In other words, the data comes right from you and works by monitoring how well and how far you drive (using either smartphones, software already embedded in your car, or a physical device).

It can be a bit difficult to retrieve the data collected through UBI programs, but insurers have found a way around that. A popular data collection method is to gather general information about the total mileage, time of day, and other events such as harsh braking and speeding that have been defined by the insurer (so they know when you try to speed past that yellow light!). (Karapiperis, Obersteadt and Brandenburg, 2015).

Since new data is continually gathered, it means that new rating factors such as driving score (how well one drives) can be created to reduce your premium. Let’s focus some more on driving score. If you meet the insurer’s criteria for “safe driving”, the driver score rating factor becomes less than 1, and multiplying the base rate by a value less than 1 will decrease the total premium and lead to savings for you.

So far, we’ve only discussed auto insurance, but what about innovation for life and health insurance? A popular product involves collecting relevant data from devices such as Fitbits and Apple Watches and using the information to offer discounts on premiums. For instance, Manulife (a popular insurance company) has started a new initiative called Manulife Vitality which uses devices such as the Apple Watch to track the health-related activities of customers. The company then offers rewards and discounts on premiums whenever members engage in activities that improve their health.

Pros/Cons

It’s easy to understand why UBI is great for consumers since it allows them to receive lower premiums, and incentivizes them to become safer drivers. But what’s in it for the insurers? Well from their perspective, implementing UBI programs allows them to easily identify low risk drivers and increase the retention levels of “good” customers by rewarding them for safer driving. On a societal level, the continued growth of UBI programs provides an incentive for policyholders to reduce their annual mileage which leads to less vehicle emissions, improves overall road safety, and creates less road congestion — so your morning commute becomes a lot less painful (Karapiperis, Obersteadt and Brandenburg, 2015).

We’ve discussed how great UBI is for insurers and policyholders, but the real elephant in the room is privacy. After all, in order to receive the rewards mentioned earlier, insurance companies require access to a lot of personal data. Right now, how insurance companies define a “safe driver” or “healthy individual” is currently unclear, and one worry is that UBI and other technology might be used to increase premiums instead of reducing them. On the other hand, insurance companies such as Desjardins have assured consumers that is not the case. In a statement to the CBC, a spokesman clarified “[the device] can’t be used to your disadvantage […] We’re only allowed to use it to determine what the discount will be. For example, each year, we ask, ‘How many kilometres do you drive every year?’ If you say, ‘I drive 20,000, and you get this device, and it shows you drive 25,000, we cannot use that information… We can’t re-rate your policy and say, ‘Well, you lied to us, you said you drive 20,000, but you actually drive 25,000.’” (CBC, 2013).

Conclusion

Despite potential controversies surrounding the introduction of technology into the insurance industry, at the moment the pros outweigh the cons. And although the protection of consumer privacy and data will continue to pose a concern, one thing remains true: this technology isn’t going away anytime soon.