Any way you slice it, data — and increasingly sophisticated data analytics — are driving the insurance industry forward in ways and at a pace perhaps unimaginable even a decade ago. Serving a critical function in the insurance ecosystem, claims management is no exception. In fact, as McKinsey sees it, “Insurers are on the cusp of a new era of claims management, one supported by rapid technological advancements.”
While there’s no doubt leading edge technology is pushing the boundaries of what’s possible in terms of intelligence and efficiency, data can’t do it all. When it comes to advanced claims management, data doesn’t replace people; it reinforces just how important harmonization is between the two.
Identifying trends accurately
The sheer volume of information that’s captured and tracked related to insurance claims make data analytics absolutely essential to making sense of it all. That’s nothing new. What is new are technologies like artificial intelligence and machine learning that can learn and function autonomously, tempting leaders narrowly focused on the bottom line to take an either-or approach between tech and human capital. However, there’s often more to the story than data trends reveal. Consider data points that don’t fit neatly into boxes, information housed in claims notes or broader contextual information, all of which need to be evaluated and synthesized by experienced claims experts to yield insights that are both accurate and actionable. Enter the symbiotic relationship between man and machine.
Avoiding costly false assumptions
While human cognitive thinking is a critical supplement to data analytics, the reverse is also true. Data science can prove or, perhaps more importantly, disprove even the most strategic assumptions of claims experts. In other words, data can show that what we think might be a trend is, in reality,not a trend — before those assumptions affect business decisions.
For example, the Governors Highway Safety Association recently reported a notablespike in speeding and reckless driving with less cars on the road during the COVID-19 pandemic. Simultaneously, at NSM Insurance Group, we were seeing a spike in auto claims activity in one of our programs. The correlation would make sense and could signal significantimplications, but after digging into our data, we uncovered the increase in claims was due to factors completely unrelated to any rise in reckless driving, allowing us to more accurately assess the changing landscape.
Fighting fraudulent claims
Predictive analytics are a great example of how data and technology are being leveraged successfully to streamline small, high volume claims as well as automate the identification of potentially fraudulent claims. These analytics are immensely valuable, but alone, they won’t get you to the finish line. When a claim is suspected as fraudulent, the investigation that follows is more art than (data) science with complexities involving lawyers, sometimes doctors and more than a fair dose of shrewd judgement. Cue the human element, with claims experts doing what data could never do on its own: advocate for both the insurer and its insureds.
The real value of progressive claims management has as much to do with looking backward as it does with moving an organization forward — including ensuring people and data are truly harmonized to realize the best of both worlds.
