How Predictive Modeling Helps Employee Benefits Insurers Win
Predictive modelling is a statistical technique that uses historical data in conjunction with machine learning to make predictions about future events and behaviours.
While predictive modelling is now commonplace in P&C insurance, it has yet to receive widespread adoption in group insurance, largely due to data constraints.
Today, employee benefits insurers can leverage their digital ecosystems to turn predictive modelling into a core competitive advantage, yielding various benefits:
- Increase profitability and close ratios
- Reduce quote turnaround time
- And respond to market trends.
In the movie Moneyball, the Oakland A’s baseball team developed a competitive advantage with a limited budget by selecting players based on computer-generated analysis of historical data rather than relying on traditional baseball metrics.
In employee benefits, insurance carriers can gain a similar advantage using predictive modelling in the group underwriting process, enabling increasing profitability, better close ratios, and more streamlined processes.
Predictive modelling uses a carrier’s historical data with machine learning to create predictions about future events and behaviours. Of course, any statistical model is only as good as its source data. The expansion of digital ecosystems and interconnectivity via APIs in the group insurance industry has on-demand access to broader data sets, enabling deeper and more complex predictive models than were previously possible.
Increase Profitability and Close Ratios
One of the key benefits of predictive modelling in group insurance is more accurate forecasting on how to price insurance products. In fact, over 60% of insurers say predictive analytics has helped them improve profitability.
From tweaking factors on rates to entire rate recalculations, predictive models can be used to optimize group pricing. Predictive models use machine learning to analyze rates on won and lost quotes in conjunction with demographic and behavioural profiles of groups to suggest 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.
A carrier can mine its own product experience data to support a predictive model that can surpass traditional actuarial methodologies. A good 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 close ratios. Predictive models will also play a more prominent role in renewal underwriting by suggesting adjustments to retain profitable customers.
Reduce Quote Turnaround Time
Predictive modelling can help carriers reduce quote turnaround time by identifying areas that could benefit from less human intervention and more automation.
For example, predictive models have been used extensively since the COVID pandemic to develop accelerated underwriting programs that limit medical testing as much as possible. Removing requirements for medical testing (e.g., fluid tests) improves the customer experience and reduces backend processing time, freeing carrier resources to write more business. Predictive models determine where these efficiencies can be realized by identifying who is most likely to misrepresent their risk based on behavioural data, among other factors.
Predictive modelling also enables strategic quote prioritization. Predictive models 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, predictive modelling 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 modelling can lower underwriting costs and enable carriers to write more profitable business in less time.
Respond to Market Trends
Predictive modelling can help insurers reveal behaviour 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.
Predictive Modelling is the Next Competitive Frontier
Today, 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. In spite of this, as more life and health insurers build out data-first ecosystems and internal data mining capabilities, predictive modelling will become the norm, as has been the case in property & 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 over the next five years by harnessing predictive models to optimize pricing and radically improve profitability.