Ready or not, the Big Data Era in insurance has arrived. If it hasn’t already, the influx of new and enhanced datasets promises to disrupt every link in the insurance value chain – from lead generation to customer retention.
Medical information, driving records, prescription histories, credit scores… and predictive models to analyze it all.
Ready or not, the Big Data Era in insurance has arrived. If it hasn’t already, the influx of new and enhanced datasets promises to disrupt every link in the insurance value chain – from lead generation to customer retention. That includes pricing for group products, and data-driven pricing holds the potential for group carriers to establish a distinct advantage in this highly competitive market.
Data-driven projects can be initiated from distinct starting points:
While a continuous exploration of applying new datasets in new ways (bottom up) serves a useful R&D role, the quickest success path more often comes from understanding the business problems most worth solving (top down). So let’s start there.
The group insurance market comes with no shortage of challenges. Among them:
Currently, group insurers start with community risk rating factors – number of employees, geographical distribution, industry, age, occupational mix, gender mix – to establish base rates. Risk classification following this first step is of course very limited and results in common rates for all groups fitting certain general characteristics.
To further differentiate, insurers apply claims experience, which is not always the most accurate risk indicator. Claims experience is a prospective rating method, relying entirely on a past period of time to predict the future. That assumes the claims are credible to begin with, which is not always the case. In addition, claims experience is of limited use in assessing group risk for industries in a state of flux (e.g., a company experiencing massive expansion or shift in strategic focus) or when the external economic environment impacts the claimant pool (e.g., a recession prompting layoffs).
Overall, current approaches to group pricing are too linear, overly simplistic, and often based solely on statistical tables.
To augment and improve current processes, insurers are now turning to a new source for insights: big data. Accessing a range of alternative datasets and applying analytics modeling can reveal a much more accurate risk profile for any given group. By taking this deeper dive, insurers can identify nuances among groups previously considered equally risky and therefore rated the same.
This higher-level differentiation delivers a range of strategic insights that can be applied to:
Data-driven pricing will make it easier for brokers to identify groups ideally suited for certain benefits and riders. It will also advance new workplace initiatives by complementing wellness tracking programs that reward healthy behaviors and aligning with voluntary benefits programs in which employees pay the premium – the more data willingly shared, the greater potential for pricing discounts for those in the top quadrant of risks.
As with any initiative, the success of data-driven pricing will require connecting its implementation to tangible business results. Key performance indicators (KPIs) that could be targeted include increased persistency, enhanced profitability, and improved customer satisfaction – with all three KPIs fueling one another.
Increased persistency: Groups who recognize their unique premium rate is well-aligned with their risk profile will stay with a carrier longer. This is especially true for lower-risk groups, the most desirable market for group carriers. For higher-risk groups, data-driven pricing will allow the carrier to ‘justify’ the higher rate and even start the discussion around proactive measures to manage increased claims and improve the group’s risk score.
Enhanced profitability: Closer alignment between group risk scores and unique premium rates will improve chances of the carrier achieving target profit margins.
Customer satisfaction: This can be viewed as an overall measure – a recognition that the group’s interests and risk profile (reflective of all the good things they are doing at the work place) are well-aligned with the insurer’s assessment of the group as a valued customer. In other words, a win-win scenario.
The terms “dynamic pricing” and “dynamic underwriting” are now commonplace for data-driven initiatives – and for good reason. Data is not static; it is continuously evolving. Therefore, the collection and analysis of this data needs to be performed on a continuous basis, and initiatives tied to data must be updated accordingly.
As big data becomes more ingrained in the group pricing process, approaches that allow flexibility and adapt easily to new forms of information will be those best suited for long-term success. Game-changing datasets and analytic models are on the way – preparing for them now will provide a competitive advantage moving forward.
