If you’re in the insurance industry and you’re not using web data to help you close claims faster by now, you’re behind the times. Most modern insurers are already leveraging web data in one way or another, though often manually and focused only on a small subset of claims incurred by a carrier. Even then, scaling up what is undoubtedly a time-consuming and resource-intensive method is next to impossible without a driving technology. How can this information be identified and used in a scalable way?
Consider three of the big challenges to managing web data:
The first and biggest hurdle in online data use is matching the data points you find to the individual in question. There may be a person with nearly identical characteristics to your claimant online, how can you be sure that this particular John Doe is your claimant? Confirming this in a reliable and automated fashion is among the biggest hurdles in using web data in the past and is now being overcome.
The second challenge is to determine whether the data you’ve found is relevant to the claim. It’s possible to find a claimant online and identify a data point about their behavior. But does it matter to the claim? In theory, an investigator can look over each data point and make a determination of value. But in practice, the volume of information is just too great. The filtering must be automated in some fashion to turn data into realistically usable information.
So now you have a great system that properly identifies claimants online and examines the relevance of the information you see— how do you stay updated on the continuous stream of new information pouring from that claimant’s activity? Not only does your claimant data need to be accurate and relevant to the claim, it also needs to be updated in real time. Just because there is nothing relevant on a Tuesday doesn’t mean that there won’t be something on Thursday.
Powerful tools are needed to unlock the potential of online data and as time goes on and technology improves, insurers will continue to gain access to increasingly large datasets found on the web. The key to successfully harnessing these new datasets is identifying when technology has reached a point that assessment of data is best handled by machines. For web data, at least, that day has come.