OMPI Blog

OMPI Blog

Opinions expressed on this blog reflect the writer’s views and not the position of the Capgemini Group

What data and analytics mean for insurers and their customers

One of the things I remember most about making the move from rural Yorkshire to London was the way my car insurer reacted. “On street parking in a narrow lane used by tractors to an off street, private access parking bay under a block of flats? Certainly, Mr Knock. Your premium will merely increase sixfold.”

This raises the most fundamental challenge – are insurers any more the guardians of “data and analytics excellence”? The hypothesis emerging is potentially not and that the core capability of assessing risk and ultimately price is rudimentary at best.

In many cases, insurers still rely on fairly crude measures such as age and postcode to determine premiums (gender having been removed as an option due to a European Court ruling a couple of years ago) and a large dose of black box stuff under the header of “actuarial”. But this is simply not enough in an age of data proliferation and associated “decision science”.

Yes, the chances of being involved in an accident in a city of 8 million people are probably higher than in a village where there were more dogs than people. But I decided not to splash out more than the value of the car in insurance, and moved insurers. All in all, the experience was a little frustrating for me, and from the insurer’s perspective, they lost a profitable customer.

But are things changing? We are constantly hearing about the increase in available data, and the power of companies to analyse this – so what?

Let’s start with the insurers themselves. At their heart, they appraise and value risk, making their decisions on what is the acceptable premium to receive for the item or event they are being asked to take on. An increase in the amount of available data, and the complexity of the analytics they can perform, could ultimately not only lead to a fundamental shift in their operating models to support that (and the immediate challenge of securing talent to do so) but even perhaps challenge the fundamental assumption of pooled v individual risk. No longer are customers buying into the philosophy that the “riskier and more likely to claim” are supported by the “less likely”.

That last point brings us onto the customer. We are all aware that companies hold vast amounts of information about us. From an insurance perspective, what we expect them to do with it is to give us a personalised, accurate quote, preferably one as low as possible and one that reflects our own, personal risk. And the quid pro quo is if I believe you will use that data responsibly and it does provide fair not pooled risk then I might just go with you. (Carrot and Ingenie have that as their underlying philosophy and are routinely exposing the narrow thinking of leading insurance brands towards young drivers, for example)

I expect my insurer to notice that I spend most of my week away on client site, not using my car, and so offer me a “weekend only” insurance deal. They should know that sometimes I drive through high risk of crash areas and other times along deserted country lanes. Insights such as these increase the chances I’ll stay with that insurer (in turn reducing their costs), and will increase my overall satisfaction with them, possibly moving insurers away from low-priced commodities to brands that we’d recommend to friends.

There is experimentation with telematics in cars and RFID chips in home appliances (I could potentially even tell the police where the thief of my iPhone is heading), however nothing compared to the quantity and quality of data interrogation from car and appliance manufacturers themselves. At the end of the day, there is a real concern that insurers are actually heading to the back of the queue in the very area where they need to be at the front.  Let’s hope that those advancements quickly arrive so that I, and customers in general, start to get value for money on our personal risk and not necessarily the price it has to be for the company to seemingly make money.  And at the same time I promise to squeeze on the brakes a little earlier and a little less sharply and look out for those humans (and dogs) crossing my road.

Deal?

About the author

Richard Knock
Richard Knock
Richard is a Senior Consultant in Capgemini’s Operating Model and Performance Improvement practice. He has worked on designing new operating models for businesses in a range of industries, most recently in the Utilites and Pensions sectors. In particular, he is interested in how rapidly changing customer expectations are forcing businesses to change the way they do the things they’ve always done.

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