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.
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.