Pricing is no longer the cost-plus game in insurance it used to be. Insurers wrestle with demands of better and more agile pricing to stay competitive amid technology fueled market dynamics. Price-comparison websites have empowered consumers to compare products and decide best choices for their unique needs. Consumers are also more amenable to novel propositions based on evolving coverage types, which require new, dynamic pricing structures.
To design innovative pricing models such as usage-based, carriers rely on advances like big data analytics (BDA) and IoT. This is a departure from traditional customer segmentation driven from limited number of easily identifiable criteria. These models glean data from new, external sources and estimate insurance pricing risk or consumer propensity to pay, buy or churn more precisely.
Personalized insurance products are defying the traditional principle of “risk pooling” which forms the core of the insurance business model. When carriers calculate the individual’s personal risk and charge accordingly, they no longer spread risk collectively among policyholders. Risk segmentation is based on actual individual consumer behavior.
Growing BDA allows insurers to better understand individual price sensitivity and likelihood to switch insurance at renewal. They reportedly charge premiums based on optimum margins they can earn from individuals rather than the associated risk and/or cost. An FCA study found that prices were being set based on where consumers shop, other products they buy and media habits. BDA are thus being used regularly to “optimize” dynamic prices. As pricing decisions involve commercial considerations, refinements in costing do not necessarily mean prices will more accurately reflect risk.
Pricing practices of insurers are routinely sub-optimal. Insurers offer deep discounts to clients at acquisition hoping price increases at renewal would be less resisted. They set cost-oriented pricing structures on claims experiences. To improve pricing strategy and realization, insurers are adopting improved risk management practices and factoring in better price sensitivity.
Pricing transformation success depends on carrier’s maturity levels, with implementation pathways varying based on typical levels delineated below.
- Application of generalized linear models(GLM) across risks.
- Automated pricing tools for high-quality risks.
- Robotics for market understanding and dynamic price adjustment.
- Product simplification for optimized pricing.
- Full-scale transformation.
Resultant pricing models cater to demand in more fine-grained ways so customers can buy options they need. Unbundling however, exposes companies to disruption especially if they cross-subsidize businesses, as insurers do, with direct sales channels.
Some insurtechs assisting carriers with pricing are Akur8 and WTW. Akur8’s solution enhances pricing processes by automating risk and demand modelling. AXA Spain has inked a partnership with Akur8 to enhance its pricing processes. WTW’s global Radar Live is a price delivery platform.
With insurers gaining from increasing pricing sophistication and ability to identify high risk characteristics, some consumers can no longer afford insurance cover. Eventually, profiling could reduce availability and affordability of insurance. Segmentation that lets insurers cherry-pick “good” from “bad” risk can lead to increasingly differentiated pricing between low- and high-risk customers.
With greater customer data, potential for more discrimination exists. It also begets the question whether improved risk pricing can drive greater profitability. While BDA can fine-tune risk classification and improve predictability of policyholders making a claim, it does not guarantee profitability.
Harnessing big data for refined risk classifications can be exorbitant, with significant investments generating marginal improvements. Costs get escalated when carriers emulate popular trends. Carriers also risk being perceived as unfair, exclusionary or discriminatory.
Markets and consumers will push carriers to adopt pricing innovations. For many, the result will be a zero-sum game, with improvements coming at the cost of reputational risks, exorbitant spends and increased regulatory oversight. But, those that master the fine balance between profitable pricing models and loyal customers, would rule the roost.
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