For policies such as home and car, location is one of the more important types of data used to calculate premiums, because of relative factors like crime rates and nearby threats.
Basically, the more accurate the risk assessment is, the more accurate your policy will be. In some cases, improving the risk assessment could mean the potential of a reduction in premium, in others it could mean spotting a risk which was previously overlooked.
What is ‘location data’?
‘Location data’ refers to electronic information about a specific location, and it is defined by the Information Commissioner’s Office (ICO). It is defined based on ‘information collected by a network or service, about where the user’s phone or other device is, or was located’.
As a consumer, you may already be familiar with location services on your mobile phone. Location data has become almost universal across modern technology, popular uses including getting directions, and ordering a cab. If you are familiar with these applications, then you may also be aware that you have to give consent to every individual app or website requiring access to them. This is because location data is collected by a network or service and, understandably, there are tight security controls surrounding it.
However, location date also includes GPS-based data, and data created outside of phone networks by smartphones, tablets or sat-navs, or collected at a local level for example by home or business wi-fi equipment. Although these categories are less regulated.
What does it have to do with insurance?
Well, this technology-driven location data is capable of far more than just pin pointing a spot on the map.
For instance, insurers could gain a lot of important context from it. Using house values as an example; insurers could get useful information from the distance of a building from coastlines, nearby business competitors, vehicle density of an area, and relevant crime statistics.
Studies conducted by Perr & Knight for Pitney Bowes, discovered that around 5% of homeowner policies and as many as 10% auto policies in America could be incorrectly priced because of unspecific location data.
Forbes contributor Hugo Moreno notes: “In many cases, the gap between the estimated and actual location is small enough to be insignificant, but where it’s not, there’s room for error—and that error can be costly.”