How Big Data is Changing Insurance Forever

The insurance industry works on the principle of risk. Customers take out policies based on their assessment of a particularly bad thing happening to them, and insurers offer them cover based on their assessment of the cost of covering any claims.

So wouldn’t it benefit everyone if there was a way to more accurately assess risks? Well, it turns out that in age of Big Data there is. Big Data as many will be aware by now is a buzzword which refers to the ever increasing amount of digital information being generated and stored, and the advanced analytics procedures which are being developed to help make sense of this data. Predictive, statistical modelling basically means working out what will happen in the future by measuring and understanding as much as we possibly can about what has happened in the past. “Models” are then built which show what is likely to happen in the future, based on the relationships between variables which we know to exit from examining the collected data from the past. It is a key tool in the Big Data scientist’s toolkit, and insurance (predictably) has been one industry that has been very keen to adopt it.

So in this article I will take an look at some of the more recent developments in the insurance industry, which have become available thanks to our increasing ability to record, store and analyze data.
One of the most important uses is for setting policy premiums. In insurance, efficiency is an important keyword. Insurers must set the price of premiums at a level which ensures them a profit by covering their risk, but also fits with the budget of the customer – otherwise they will go elsewhere.

A great example of this formula in action is motor insurance. While drivers (particularly younger ones) often complain about the high prices, this is a market where there is a huge amount of competition and shopping around on price comparison services is common among customers. As a result an insurance business is made or broken on its ability to accurately assess the risk posed by a particular driver and offer them a competitive, but profit-making premium.