Covering Disruptive Technology Powering Business in The Digital Age

This article was originally published by forbes.com and can be viewed in full here

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.

Many insurers now offer telemetry-based packages, where actual driving information is fed back to their system to a personalized, highly accurate profile of an individual customer’s behavior can be built up. Using predictive modelling as mentioned above, the insurer can work out an accurate assessment of that driver’s likelihood to be involved in an accident, or have their car stolen, by comparing their behavioural data with that of thousands of other drivers in their database. This data is sometimes captured and transmitted from a specially installed box fitted to the car or, increasingly, from an app on a driver’s smartphone.

US insurers Progressive offer a great example of a business which has committed to working with data to enhance its services. It has created what is calls its Business Innovation Garage where technologists known as “mechanics” produce and road-test innovations. One project involves rendering 3D images of damaged vehicles using computer graphics. Images are scanned in from cameras to create 3D models allowing structured data to be recorded on the condition and damage to a vehicle.

A similar revolution is underway in the world of health and life insurance due to the growing prevalence of wearable technology such as the Apple Watch and Fitbit activity trackers, which can monitor a person’s habits and provide ongoing assessment of their lifestyle and activity levels. According to research by Accenture, a third of insurers are now offering services based on the use of these devices. One of these is John Hancock, which offers users discounts on their premiums and a free Fitbit wearable monitor. Customers can work to reduce their premiums on a sliding scale by showing that they are improving on their unhealthy behaviors. Brooks Tingle, the company’s SVP of insurance marketing and strategy, says “customers don’t mind giving up some data if you’re transparent about what data you’re asking for, and they are getting real value back for it.”

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