Skip to Main Content

Knowledge Center

Innovation

A Call for Pricing Precision: Why Dynamic Risk Pricing is the Future of Group Insurance

Precision_Pricing_Dartboard

When it comes to risk pricing in group insurance, carriers are seeing double… and triple… and quadruple.


Many are relying on similar rating factors and arriving at the same indistinguishable price quotes. Unable to differentiate product offerings, carriers face a future of highly saturated, low-growth markets and commoditized pricing.

Pricing precision can help insurers escape this fate – but it won’t be easy. Carriers must contend with unhealthy and aging populations and significant workplace demographic shifts. The insured population is growing older, working longer, and inflating medical costs. At the same time, growth from an untapped middle market remains frustratingly out of reach. A lack of financial literacy, coupled with rising customer expectations for speed and simplicity, contribute to a persistent insurance protection gap. 

The good news? Data can help move the market beyond one-size-fits-all pricing models to reflect risk accurately. The availability of large quantities of information about the health and wellness of insureds makes precision pricing possible for the first time in the group marketplace. 

The premise is simple: New data-based technologies can unlock insights into the changing risks and coverage needs of a particular group (or even a segment within that group), empowering insurers to price risk far more accurately. This dynamic (or granular) pricing approach relies on data analytics to enhance offerings and expand into underserved markets. 

The Time is Now

Consider small and medium-sized employer groups. Quoting based on such small risk pools presents unique and obvious challenges. After all, without the ability to spread risk, carriers must plan for greater anti-selection. Underwriting costs rise, forcing the insurer to adjust product designs and pricing to unaffordable levels, accept shrinking margins, or shun business. This trend not only reduces options available to these smaller groups, but cuts off opportunities for brokers who would otherwise pursue this business. Dynamic pricing is tailor-made for these smaller risk pools, enabling more granular, accurate, and affordable quotes; more efficient underwriting; and wider distribution. 

Group insurers have been slow to respond, and most remain dependent on inefficient underwriting processes and sales from a shrinking intermediary- and agent-based workforce. Of course, data can’t solve every dilemma in the group marketplace, and any effort to implement dynamic pricing should begin with a clear-eyed understanding of what data can, and cannot, do. The so-called Hype Cycle of emerging technologies, first coined by the research firm Gartner, is relevant. Innovation, Gartner suggests, begins with the spark of an idea, surges to a peak of wildly inflated expectations, followed by a “trough of disillusionment” as barriers emerge. One such trough may be dead ahead, as the  “gravitational pull of big data is now so strong that even people who haven’t a clue as to what it’s all about report that they’re running big data projects.”  

Still, at a time of increasing commoditization, carriers could benefit from asking important questions about pricing practices and explore more data-based approaches. Indeed, as traditional product designs, distribution channels, and underwriting approaches are disrupted, there has never been a better time to challenge the status quo. 

Contact RGA  to learn more about dynamic pricing in the group space. 

Coming soon: Part 2 in the article series: “Every Step You Take: Data Can Fuel Stronger Employee Engagement and Better Pricing"


The Author

  • Anil Sanwal
    Vice President, Group Products
    Global Group Reinsurance Division
    RGA International Re

Summary

This is the first installment in three-part series of articles about trends in group insurance regarding data collection and analysis. “A Call for Pricing Precision” argues for moving away from a “one size fits all” pricing model toward a deep data dive to improve pricing accuracy. 
  • big data
  • data analytics
  • data sources
  • dynamic pricing
  • group insurance
  • group reinsurance
  • pricing
  • product design
  • Product Development