Risk models are an increasingly critical component to the modern insurance carrier. They are relied upon for all phases of insurance, from marketing and sales to pricing, claims, and operations.

Executive Summary

Chris Cooksey, senior director of advanced analytics at Guidewire Software, writes that innovation in risk modeling requires understanding the role data scientists play in the process. As risk models become increasingly important to modern insurance carriers, he writes that hiring and management as well as maintenance and upkeep are important factors in keeping business processes moving forward.

After more than two decades of working with insurance carriers on their risk models, it has become apparent to me that there are some common mistakes insurers make in the management of their analytics programs. It is clear that to get the most value out of their risk models, carriers need to more actively manage their data scientists and do so in a manner that suits the core capabilities of these professionals, as well as the core needs of the business.

Hiring and Management

Perhaps the most unique piece of the analytics puzzle is the hiring and management of personnel – the predictive modelers themselves. The mathematical background required for predictive modeling, and the experience and understanding of the issues involved, are critical and not commonly nor easily found. These human resources must be intentionally hired for and developed.

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