“Such practice, however, raises concerns about the privacy of data stored in publicly available Google Street View, Microsoft’s Bing Maps Streetside, Mapillary, or equivalent privately held data sets like CycloMedia,” say Kidziński and Kita-Wojciechowska. Of course, Google is not the only company to collect street-level images. This ability has outstripped most people’s understanding of what is possible with their data, and it has certainly outstripped the speed at which legislation can be passed to control it. The ability to collect, analyze, and exploit information has increased dramatically in recent years. If insurance companies can benefit, why not other businesses? “The insurance industry could be quickly followed by the banks, as there is a proven correlation between insurance risk models and credit-risk scoring,” say Kidziński and Kita-Wojciechowska. “The consent given by the clients to the company to store their addresses does not necessarily mean a consent to store information about the appearance of their houses,” say Kidziński and Kita-Wojciechowska.Īnd the approach could open a Pandora’s box of data analytics.
GOOGLE MAPS PHOTO VIEW COUNT ZIP
“We found that features visible on a picture of a house can be predictive of car accident risk, independently from classically used variables such as age or zip code,” say Kidziński and Kita-Wojciechowska.Īn interesting question is whether they gave informed consent to this activity and whether an insurance company can use data in this way, given Europe’s strict data privacy laws.
It turns out that a policyholder’s residence is a surprisingly good predictor of the likelihood that he or she will make a claim. Finally, the researchers number-crunched this data set to see how it correlated with the likelihood that a policyholder would make a claim. They classified this dwelling according to its type (detached house, terraced house, block of flats, etc.), its age, and its condition. To find out, the researchers entered each policyholder’s address into Google Street View and downloaded an image of the residence. The question that Kidziński and Kita-Wojciechowska investigated is whether they could make a more accurate prediction using a Google Street View image of the policyholder’s house.
GOOGLE MAPS PHOTO VIEW COUNT CODE
The insurer also shared its own prediction of future claims, calculated using its state-of-the-art risk model that takes into account the policyholder’s zip code and the driver’s age, sex, claim history, and so on.
Each record included the address of the policyholder and the number of damage claims he or she made during the 2013–’15 period.
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