Collecting relevant data and analyzing it to identify both potential risks and opportunities is nothing new to insurance companies. In fact, insurance actuaries been early adopters of the most cutting-edge statistical analysis tools available since insurers were first writing policies on cargo transported by sailing ships and trade caravans. It was those early efforts in analytics that enabled insurance to exist and thrive as a business model, playing its critical yet deeply underappreciated role in the growth and stability of economies throughout the centuries.
But while the core concepts might be familiar to the actuaries and insurance firm owners of yesteryear, the risk-reduction capabilities of today’s big data and machine learning-powered insurers would look like magic. In fact, we don’t even need to go back to the era of sailing ships to see how much has changed, as the big data revolution of the past few years has fueled a new renaissance of insurance risk assessment and management with no end in sight to its potential.
While there are many factors in play, one of the most notable and powerful is the ability to break out of both internal silos and the insurer’s own walls to incorporate data that was never before able to play a role in actuarial analysis. Today’s analytics tools aren’t walled off from internal data sources such as marketing and customer service; instead they inform – and are informed by – those rich sources of information and potential insight.
Insurers are also quickly coming to realize how much value there is in third-party data sources. This can be anything from well-known data brokers such as credit bureaus to data sharing arrangements with institutional customers or even individual policyholders themselves. In addition to uncovering new insights and providing a new perspective, these third party data sources also provide a critical check on insurers’ own internal data, identifying potential fraud, waste and abuse by offering another window into their policyholders’ lives and activities.
All of these tools and capabilities allow for earlier identification and assessment of risk, either reducing the resources necessary to address it or potentially allowing an insurer to avoid taking on toxic and unprofitable risk altogether. They also reduce the cost of risk assessment to the point where insurers are able to adjust lower-cost claims than ever before, finding savings in formerly too-small-to-address claims without sacrificing resources dedicated to larger, more high-value claims.
What does the future hold? Insurers have always worked to help institutional policyholders avoid risk and losses, and health insurers have long known the value of sharing their insights into how to live a healthy lifestyle – and avoid big health insurance claims. Thanks to big data analytics operating in real time, one next step for insurers may well be taking more of an ongoing and proactive role in helping both institutional and individual policyholders identify and avoid specific risks for the benefit of all parties. Just like the earliest insurers invested in eradicating the pirates who threatened their policyholders’ shipping interests, the insurers of tomorrow may apply the power of their cutting-edge analytics to identifying and addressing a whole host of risks before they ever cause losses and become claims to be paid.
Interested in seeing what cutting-edge analytics can do to reduce risk for your organization? Contact us today.