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The data scientists and analytics experts needed to develop predictive models can be costly, but when you are starting out and there is a knowledge gap, it is a necessary investment. However, once the knowledge and experts are available in-house, further applications of predictive analytics become less costly, and programs are implemented more smoothly. (Credit: everything possible/Shutterstock) The data scientists and analytics experts needed to develop predictive models can be costly, but when you are starting out and there is a knowledge gap, it is a necessary investment. However, once the knowledge and experts are available in-house, further applications of predictive analytics become less costly, and programs are implemented more smoothly. (Credit: everything possible/Shutterstock)

Most insurers have adopted risk modeling in their business to one degree or another by now, but despite early successes, many are failing to adjust and evolve and are missing out on significant savings and value.

Early in the adoption of risk models, insurance companies tended to build in-house and often focused on pricing models for a large line of business. At this point, risk modeling was new to insurance, and the stakes were high. As such, considerable effort was put into identifying and acquiring data scientists and analytics experts to develop these models. Understanding this was not their area of expertise, insurance leaders created internal research and development (R&D) groups to develop and apply these risk models.

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