How Can Geolocation Data Assist the Financial Services Industry?

On the face of it, geolocation data has very few applications within the financial services sector. Although some insurers have been using artificial intelligence (AI) algorithms for a while now, geolocation technology has not been a big part of what financial services have been about.

For example, with car insurance, underwriters have generally wanted to know where a car is parked overnight by zip or postcode rather than its movements on a moment-by-moment basis. However, some forward-thinking fintech companies are now starting to come up with applications for geolocation data which may have a significant effect on consumers in the near future. What are they?

Car Insurance
As mentioned, car insurers are increasingly looking at geolocation data as a means of assessing how much drivers should pay for their premiums. This accounts for not just how much they drive but when they do so. Are they driving at high-risk times or do they frequently take routes which include known black spots? Equally, do they drive sensibly and under the speed limit? All of this can be established with big data AI so long as the relevant data is collected in the first place. Of course, it is not just car insurance but motorbike insurance and commercial vehicle insurance that could be affected by this technology, too.

Travel Insurance
Many insurers provide insurance for travel but they don’t always reach the market they’d like to. How about geolocation data coming from a smart device that informed your household insurance company that you were about to board a plane or that you had just touched down in a foreign country? This could feasibly be used as an upselling tool by insurance companies to target a specific package based on your travel plans. Consumers would benefit by a more tailored service which meant they were not paying for things they didn’t need, too.

Weather Risk Mitigation
Another bright idea about geolocation data is that it could be tied into the latest meteorological information. This could help car and travel insurers to warn their clients of impending weather events that might affect them, thereby lowering risk. In addition, such information might be able to be used by life insurers because it would help people they cover to avoid severe weather events like hurricanes. Overall, the use of geolocation data would mean that fewer large payouts for catastrophic events would occur. If enough people signed up to such a scheme where they would receive personalised advice based on their exact whereabouts, then it would be possible for financial services companies to lower their life insurance premiums. Few consumers would complain about that so long as their locational data remained private, of course.

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