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    Calculating Insurance Risk

    Calculating Insurance Risk

    Digital Tech in the Insurance World

    People have remarked that the insurance business can be slow in adapting to new technology. If that was ever true in the past, it certainly isn’t today. The industry uses some of the most sophisticated analytic tools to calculate insurance risk and create fair rates for those insured. A technique called “predictive modeling” is the basis for insurance data today. Here’s how it impacts you, the insured.

    Predictive Modeling

    Investopedia defines predictive modeling as “the process of using known results to create, process and validate a model that can be used to forecast future outcomes.” This modeling uses what businesses call “big data,” the wealth of digital information available for analyzing and predicting based on consumer behavior as well as historical evidence. In the insurance world, this comes in the form of global linear models (GLM’s).   GLM’s are the industry standard for pricing, according to the Insurance Business Journal.

    GLM’s make it easier to individualize the risks and needs of the insured.  Instead of relying on general statistics, some of the predictors utilized today are prior claims, financial attributes, and geological area. The Insurance Business Journal says they’ve become an industry standard because of the transparency of their results.

    The Actuarial Review highlighted how this new method impacts insurance clients directly. “Today, insurers use a variety of predictive analytic tools to hunt through large data sets to find variables that hold clues to individual customers’ riskiness and purchasing behaviors.” The good news is predictive modeling is very individualized to help calculate how each customer policy should be built, according to their risk factors. Positive actions like paying bills on time can even play a role in calculations.

    Modeling and the Consumer

    One thing that is changing more with the depth of GLM modeling is the use of customer behavior as a predictor. To this end, some auto insurers have even offered a feedback device that resides inside the insured’s vehicle and reports driving habits. This could add to your individual data to help determine rates—just make sure you drive on your best behavior.

    Social media is also added to the mix, but more for the insurer’s purposes. Analyzing social channels can help an insurer learn what insurance choices consumers prefer. How brands interact with and are received by consumers can largely influence the insurer.

    Predictive Modeling and the Future of Insurance

    Digital, and data-driven models are now a norm in the insurance world. However, these insurance risk-calculating analytics may benefit consumers now more than past practices. Highly individualized risk factors as well as a focus on positive records may help the insured receive the most fair rates that also allow for more beneficial coverage.

    From flooding predictions to identifying driving trends, digital data can be a great asset to both the insurer and the insured. Increased use of data analysis may answer questions like “How we can lessen auto claim severity?” and “Is there a better way to predict potential fraud?” As the pool of data gets larger, so do the implications for modeling and in turn, consumer rates and policies. Welcome to the future.

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