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.

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