If you’re just catching on to the fact that big data is shaking up the insurance industry in a big way, you’re a bit behind the curve.
Across all industries, almost 90 percent of large companies say big data is going to revolutionize business operations, according to a report from Accenture. It’s predicted that this revolution will be on a scale comparable with how the internet changed the way we work starting in the 1990s. Remember the way it was before the days of email, Google and filing claims online?
Understandably, companies are scrambling to identify the new realities big data will bring, and seize the opportunity it presents. According to that same Accenture survey, 83 percent of companies plan to pursue big data projects in order to try to gain a competitive edge.
We have already seen an inkling of big data’s impact, especially in claims departments that are increasingly finding themselves on the front lines of these developments. From telematics to text mining, experts see data analytics significantly reshaping the claims process in areas like fraud detection, claims triage and process efficiency.
But organizations looking to successfully develop and implement new data-driven processes still face considerable hurdles, especially when it comes to cost. Collecting, analyzing and even storing all the data insurers collect can be prohibitively expensive.
Currently, insurers only process about 10 to 15 percent of the structured data they have at their disposal. That structured data, which is already sorted and organized in databases, is easier and cheaper to manipulate than unstructured data. The trouble is that an estimated 80 percent of all data businesses use is unstructured.
In the claims world that figure may be even higher. Police reports, witness accounts, claimant statements and other adjuster notes all need to be processed and cleaned up (data pros call it scrubbing) before it can be put to actionable use.
Data scientists and insurance professionals are turning to text mining as a relatively cost-effective way of uncovering the value hidden in these mountains of unstructured data.
Text mining encompasses extracting useful or interesting information from unstructured text. In its most basic form, text mining involves scanning large amounts of data for keywords or phrases, similar to a Google search.
But thanks to artificial intelligence advancements such as natural language processing and decision logic, modern text mining goes beyond simply finding information to analyzing the documents for significant facts and relationships within the data.
Today, text mining is capable of scanning reports and interpreting adjusters’ handwriting. It can discover customers’ sentiments, from their opinion of a product to how they’re feeling immediately after filing a claim. The sources of this data are increasingly expanding beyond information directly collected during the claims process. Rather than having an adjuster sift through a claimant’s Facebook or Twitter profile, a text mining algorithm can search all of the person’s social media content in real time for information that contradicts or confirms specific details of a claim.
These text mining tools have the potential to significantly increase efficiency within the claims department. Analyzing claim filing call transcripts can reveal better ways of structuring and scheduling call center operations. Identifying keywords used in reports that indicate a high potential for a complicated claim, allegations of bad faith or litigation could get special attention or be assigned to a more experienced adjuster earlier in the process.
Text mining of claims data can also be an extremely useful tool in developing new products and plugging gaps in existing coverage that may be negatively affecting customer satisfaction and renewal rates.
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