Assembling the 'Justice League' for predictive analytics

Organizational buy-in is crucial to applied analytics in insurance. (Photo: Shutterstock)

Deciding what data to use and how to incorporate third-party data effectively is the most important piece of any analytics project. Following the bread crumbs of this decision typically leads insurers to weigh the pros and cons of building their own analytics model or working with an outside tech provider. The aptly named “Build vs. Buy” dilemma involves a variety of intersecting decisions and considerations, including the availability of talent and infrastructure, time-to-market, breadth and depth of in-house data, regulatory challenges and an insurer’s unique competitive position.

Over the last decade, predictive analytics have shifted from key differentiator to table stakes, as companies realize they cannot remain competitive without predictive models to guide risk assessment and pricing. Most companies assume a hybrid approach to the “build vs. buy” concept, developing their own analytics team to work with third party technology providers to build a sustainable predictive analytics strategy.