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What is it?

The uncertainty around price optimization starts with defining what exactly it is — there is no generally accepted definition. In a 2015 white paper, the NAIC Casualty Actuarial and Statistical Task Force defines price optimization as "the process of maximizing or minimizing a business metric [such as 'marketing goals, profitability and policyholder retention'] using sophisticated tools and models to quantify business considerations."

Essentially, price optimization boils down to the use of factors not directly related to risk in establishing rates and premiums. The Insurance Information Institute offers a classic example of basic price optimization: the cost of auto insurance for young drivers. Young drivers typically pay a lot for car insurance, but insurers usually limit their rates in order to keep their parents as customers. Insurers subsidize costs not based on risk but in the interest of retaining customers — that's price optimization. I.I.I. points out that some insurers have been using this kind of price optimization for a long time, and regulators have never objected to it.

(Photo: iStock)

Here's another example that gets at some of the more controversial aspects of price optimization:

Two customers with the exact same risk profiles have policies that are up for renewal. Customer A has been with the insurer for 10 years and has never questioned or complained about the price she pays. Customer B has been with the insurer for five years and has threatened to switch to a competitor several times. Insurers may use price optimization strategies to generate different rates for each customer even though they have the same risk profile.

This form of price optimization is based on the economic principle known as elasticity of demand — in this context, how much the price can go up before a consumer refuses to pay. In the era of big data, insurers can buy access to exponentially larger amounts of customer data, from their credit histories or cable providers to when they last upgraded their cell phones. The controversy stems from entering this information into complex algorithms to determine how much of a premium increase a customer will accept before deciding to look elsewhere.

Who’s doing it?

Although airlines and hotels build their business around price optimization, the practice is far less widely accepted within the insurance industry, and the vague definition makes it difficult to determine how many insurers currently engage in it. In 2013, price optimization software company Earnix released a report claiming 26 percent of North American insurers use price optimization. In that same year, however, Willis Towers Watson, which also sells price optimization software, found that only 12 percent of insurers use price optimization in personal lines and that no commercial lines respondents use the practice.

(Photo: iStock)

Is it legal?

As of May 2016, notices that limit or ban price optimization in the insurance sector have been issued by Washington, D.C. and 18 states: Alaska, California, Colorado, Connecticut, Delaware, Florida, Indiana, Maine, Maryland, Minnesota, Missouri, Montana, Ohio, Pennsylvania, Rhode Island, Vermont, Virginia and Washington.

Two notable lawsuits challenging insurers' use of price optimization are making their way through the California and North Dakota court systems, but one law firm says that so far, these cases "raise more questions than they answer." For now, the legality of price optimization is still very up in the air and depends on specific state regulations and the exact actions an insurer takes under the umbrella of price optimization.

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Legal issues aside, price optimization as it applies to insurance hasn't had the best reputation of late. Last year, NPR published an article on price optimization titled "Being A Loyal Auto Insurance Customer Can Cost You." Consumer Reports published articles on price optimization in car insurance rates and even has a petition with the tagline "price me by how I drive, not by who you think I am!"

But price optimization advocates argue that the practice doesn't automatically mean higher prices for customers. In some cases, price optimization may result in lower costs, such as when an insurer wants to attract more customers from a specific demographic or when its competitors are offering lower prices. Many insurers claim that new forms of price optimization are part of a natural progression of pricing structures driven by big data analytics.

NAIC's white paper ultimately recommends that "two insurance customers having the same risk profile should be charged the same premium for the same coverage." Nonetheless, it's clear that there's still a lot of debate around price optimization as the industry and regulators work to figure out exactly how to define it — and whether it belongs in the insurance world.

What do you think?

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Susan Kearney, CPCU, ARM, AU, AAI, is a senior director of knowledge resources at The Institutes. Opinions expressed in this article are the author's own.