The rise of Big Data has been a game-changer for an increasing number of industries, and that trend shows no sign of abating. The insurance industry, which is always looking for even more information on its customers, is a natural candidate for the transformative powers of Big Data. Some of those changes will benefit the companies, but it’s altogether possible that consumers may also get some good out of them as well.
The thing to bear in mind when talking about Big Data is what the term means. Big Data is a huge amount of information that comes from a massive number of sources, and delivered quickly. Sources of Big Data range from traditional methods such as consumer questionnaires to data transmitted via RFID tags.
As the article “Actually, It’s More than Actuarials: Big Data for Insurance” points out, bad habits that are potential high-risk factors for insurance companies may soon be routinely spotted and monitored, thanks to Big Data.
Have you seen those annoying Progressive car insurance commercials that feature the “Snapshot” device that plugs into a vehicle and transmits data back to the insurance company? The principle behind this is that the on-board device sends information on the individual’s driving habits, and this in turn could result in savings. This is a classic case of not only Big Data, but also the Internet of Things.
Big Data information can also identify areas that demonstrate consistent high-risk for auto theft, and adjust the premiums accordingly for those who live in those locales. Conversely, areas with little to no auto theft can also be identified, with theft insurance adjusted correspondingly downward.
And of course, let’s not forget the obvious source of driver data: The Department of Motor Vehicles. If anyone has information on drivers’ habits as measured by moving violations, it’s them, and if any organization has massive amounts of bureaucratic information that’s right up the alley of Big Data, it’s the DMV.
All of this data taken together could help auto insurers come up with more personalized policies that reflect the true conditions of the insured, with premiums lowered or raised depending on the verifiable risk factors presented through the data.
Homes are subject to robbery, vandalism, fire, and damage during severe weather such as floods. By employing meteorological data, for instance, insurance carriers can offer flood insurance policies that are based on sound information regarding flood risk. After all, why should a homeowner who has only the slightest risk of flood damage be charged on the same scale as the homeowner whose home sits on the shore of a flood-prone lake?
Health insurance companies are always looking for new ways to create an accurate picture of their customers’ lifestyle habits. With Big Data, a health insurance carrier can get their hands on medical examination and lab tests, prescription drugs, and other personal data. Not only that, consider how information like you having a gym membership (and evidence that you actually use it!) could weigh in your favor.
It can be a little disturbing to realize that Big Data can enable the health insurance industry to closely monitor your lifestyle and activities. On one hand, just like the auto insurance mentioned previously, it could mean lower premiums. On the other, well, read on …
Risks And Rewards
According to the article “How Big Data Is Changing The Insurance Industry Forever”, there was a case where a woman’s health insurance payouts based on her inability to work due to depression were cut off because her insurers found a picture of her on Facebook, where she was … (gasp!) … smiling!
Granted, the health insurance industry is looking for ways of combatting fraud, and yes, there are lots of people out there who love to game the system, but to what lengths can or should an insurance carrier go in assessing your risks? What’s next? Noting that you ate at McDonalds too many times in one month, so you’re at risk for obesity-related conditions? Or perhaps an auto insurer notes that most of the music you choose to play in the car is related to driving fast, thus posing an added temptation to speed? Where does it end? How much privacy do we have to give up in order not to go broke paying for insurance coverage?
On the other hand, Big Data provides insurance companies with the means of detecting fraud, and by reducing the waste of fraudulent claims, the savings is theoretically passed on to us. Access to Big Data can also make sure those mitigating circumstances that couldn’t be verified before will now be taken into account. The ability to underwrite an insurance policy for two different 50-year old men and being able to distinguish between the one who works out regularly and watches his weight, and the one who is chronically overweight, and smokes and drinks to excess is pretty attractive. After all, why should the man who’s doing all the right things find himself in the same risk category just because both men share a similar age?