Moneyball for Employee Benefits Carriers

Employee benefits carriers can differentiate themselves and shield themselves from disruption by harnessing predictive models to optimize pricing and radically improve profitability.

Baseball in glove with other baseballs behind it

In the movie Moneyball, the Oakland A’s baseball team wins a big competitive advantage over wealthier teams by selecting players based on sophisticated computer-aided analysis of player data rather than relying on traditional baseball stats. Now, every team does it.

In employee benefits, insurance carriers can gain a similar advantage using predictive modeling in group and voluntary underwriting, enabling increased profitability, better closing ratios and more streamlined processes. 

Predictive modeling combines a carrier’s historical data with machine learning to create predictions about future events and behaviors. But any statistical model is only as good as its source data. The expansion of insurers’ digital ecosystems and connectivity via application programming interfaces (APIs) now offers on-demand access to broader data sets. Enhanced data enables more accurate predictive models.

Increase profitability and closing ratios

A key benefit of predictive modeling is more accurate pricing of insurance products. Over 60% of insurers say predictive analytics has helped them improve profitability. However, many of them are property and casualty insurers. Benefits insurers are behind but starting to catch up. 

From tweaking factors on rates to entire rate recalculations, predictive models can optimize group pricing. They use machine learning to analyze rates on won and lost quotes in conjunction with demographic and behavioral profiles of groups to recommend pricing improvements. 

While incorporating external data into a predictive model is valuable, there are limitations to this approach: It can be costly and time-consuming.  

It’s less expensive and faster for a carrier to mine its own product experience data to support a predictive model that can surpass traditional actuarial methodologies. A sound predictive model can assess multiple data points simultaneously, identifying important trends and correlations.  

Additionally, predictive models can provide actuarial and pricing teams tools to identify trends and key factors in both won and lost cases to improve closing ratios. These models will also play a prominent role in renewal underwriting by suggesting adjustments to retain profitable customers.

Reduce quote turnaround time

Modeling can help carriers reduce quote turnaround time by identifying areas where human intervention can be minimized.

For example, predictive models have been used extensively since the COVID pandemic to develop accelerated underwriting programs that limit medical testing for group or voluntary benefits. Removing requirements for fluid or other medical tests improves the customer experience and reduces back-end processing time, freeing carrier resources to write more business. Models can determine where these efficiencies can be realized by identifying who is most likely to misrepresent their risk based on behavioral data and other factors. 

Predictive modeling also enables strategic quote prioritization. It can prioritize quotes based on their profitability over a set number of years and their overall likelihood to close rather than a crude assessment of the due date and quote complexity. 

Furthermore, modeling can help insurers include only the most essential questions on digital medical questionnaires to streamline the voluntary benefits application process and maximize the revenue opportunity. 

By increasing automation levels, predictive modeling can lower underwriting costs and enable carriers to write more profitable business faster. 

See also: How Insurers Are Applying AI

Respond faster to market trends

Predictive modeling can help insurers reveal behavior patterns and common demographics that expose opportunities for increased market penetration. 

For example, a predictive model can identify optimal pricing and plan designs for underserved markets based on historical won/loss data – broken down by geography and industry. 

With this data, carriers can launch more targeted marketing initiatives and increase their probability of closing new business. Carriers can use this data to develop market penetration strategies that target underserved groups with tailored plan designs and pricing to make them profitable. 

Additionally, as voluntary benefits become an increasingly important piece of group benefits strategy, insurers will rely on predictive models to determine which products to offer plan members in response to market trends. Will today’s hot products be hot in the future, or will others be in demand? Modeling provides insights.

Predictive modeling: the next competitive frontier

Predictive analytics is everywhere. McDonald’s uses predictive models to determine the most profitable locations for new restaurants, and Netflix uses sophisticated models to recommend your next TV binge. 

For group benefits insurers, having the right data (and the right amount!) will always be a challenge. Despite this, as more life and health insurers build out data-first ecosystems and internal data mining capabilities, predictive modeling will become the norm, as has been the case in property and casualty insurance for several years now.

The insurance space is more competitive than ever. Employee benefits carriers can differentiate themselves and shield themselves from disruption by harnessing predictive models to optimize pricing and radically improve profitability. They can be like the Oakland A’s instead of the disadvantaged competition. 

Stephen Boucher

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Stephen Boucher

Stephen Boucher is an account executive at Global IQX, the leading provider of AI-driven sales and underwriting solutions for the group insurance industry in the U.S. and Canada. He writes about emerging technologies, digital transformation and artificial intelligence.


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