Driving Motor Insurance Ahead With Telematics
Brittany Detamore 270006ARTU email@example.com | 2013-05-28 14:42:19.0 | Tags:  telematics usage-based-insurance analytics big-data motor-insurance insurance | 1 Comments | 6,824 Visits
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Motor insurance policies are traditionally priced on forecast risk - using rating factors such as number of miles you might drive, where you live, your age, the engine size and what you will use the car for - customers then pay a premium based on these values - however the introduction of Telematics or UBI (Usage Based Insurance) is changing this. This is not new, some insurers have tried this in the past and some in the US and Italy have offered these products for a few years. In the UK it is beginning to take off. It is currently perceived as a niche product mainly aimed at young drivers - where the black box in the car can be used to help improve driving, which reduces risk and therefore reduces premiums – and makes the parents of the young drivers happy!
An Insurer requires a variety of capabilities to be in place in order to successful deploy and manage a Telematics solution. For example, enhanced customer insight and marketing capability is required in order to manage the different touch points an Insurer will have with its customer.
At the core of this different operating model is data. Telematics requires data in order to provide the customer with a specific premium, it is required in order price that premium correctly but it is also there to support other business functions.
In order for the business to leverage this, an Insurer needs to consider how it will integrate the new source of data into its existing data architecture. We wrote a paper to outline the uses of Telematics data and how to get the information foundation correct, the steps for this include:
I have heard a lot recently about context of the data and an evolving set of rating factors for the data. One example I heard was that if someone is driving very fast on the motorway at 3am in the morning,this is more or less risky than someone driving at 30mph down Regents Street during rush hour. Of course it is probably more risky to be driving to the speed limit in London in rush hour as the average speed in London is 11mph, so the context of when you drive and where you are driving needs to be taken into account. In addition, how can you use that data to introduce a familiarity factor to the pricing? Is someone a better (or lower risk) driver if they always drive on the same road day after day on their way to work as they will be familiar with the bends, the pot holes and the potential blind spots on the roads? Or, is the better (or lower risk driver) the person who drives on a variety of roads and doesn't necessarily know the roads as well?
Clearly this is an evolving picture, but one thing is sure; data is aiding and driving Insurers ability to come up with innovative and competitive ways to price Telematics policies, which has to be a good thing for customers!