There’s a lot of talk right now about how big data is going to change insurance. Artificial intelligence and prescriptive analytics are definitely coming, and they will definitely change the insurance industry. To get a better idea of the specific ways in which big data applications are going to play out, it’s worth examining how data analytics are being used in the industry right now.
Many insurers’ initial forays into the world of big data focus on fraud detection. That makes sense — the Insurance Information Institute (III) estimates that 10 percent of property-casualty insurance industry losses each year are attributable to fraud, to the tune of $32 billion.
And most believe the problem is getting worse: 61 percent of property-casualty insurers report that the number of suspect frauds increased slightly or significantly over the past three years, according to a 2016 white paper issued by the Coalition Against Insurance Fraud. Insurance companies have always relied on technology to fight fraud. The III reports that 95 percent of insurers say they use antifraud technology, but about half say a lack of information technology resources prevents them from fully implementing it.
Enter big data. At the beginning of 2016, Towers Watson reported that 26 percent of insurance companies were using predictive analytics to address fraud potential. In the next two years, that number is expected to jump to 70 percent—more than any other big data application.
As data scientists team up with claims department leaders and other insurance professionals, many are looking to text mining as a crucial analytical tool for decoding enormous amounts of unstructured data.
Put simply, text mining is a way to scan large amounts of data for keywords, not unlike a web search. But claims departments are putting the technology to more sophisticated use, analyzing information for more significant data points and connections. Text mining can interpret claims adjusters’ handwritten notes and scan a claimant’s social media accounts for suspicious activity in nearly real time.