The use of price optimization in insurance pricing is drawing increased attention and could be the next big industry battle.
Or it could not be.
Consumer groups and a growing contingent of states are labeling the practice as unfairly discriminatory and restricting it. Insurers say there is confusion over exactly what price optimization is and claim that these same watchdogs have for years approved elements of what some are now calling price optimization.
Meanwhile, agents are acting like they hope the whole controversy will just fade away and they may get their wish.
While a full definition is hard to come by, price optimization generally refers to an insurer’s practice of varying rates based on non-risk-related factors. Price optimization involves analysis and incorporation of data not related to expected cost for risk characteristics 鈥� that is, it involves factors not related to expected loss and expense experience. Such data may include the prior year changes in premium and whether policyholders renewed subsequent to such change.
The exact number or identity of insurers using price optimization is unclear; however, nearly half (45 percent) of large insurance companies and 26 percent of all insurance companies in North America currently optimize prices, according to a 2013 survey by Earnix, a software provider of price optimization products to the insurance industry.
Regulators in 11 states (California, Delaware, Florida, Indiana, Maine, Maryland, Ohio, Pennsylvania, Rhode Island, Vermont and Washington) and the District of Columbia have issued bulletins prohibiting or restricting the use of price optimization in personal lines ratemaking.
A task force of casualty actuaries of the National Association of Insurance Commissioners (NAIC) has taken on the issue as well. In October, it issued an updated price optimization white paper that provides background research, identifies potential benefits and drawbacks of its use in personal lines, and presents options for state regulatory responses.
What Is Price Optimization?
Price optimization is not a new concept 鈥� it has been used in the retail and travel industries for years. But there is no widely accepted method and definition of it in the insurance industry.
Some refer to price optimization as relying on predictive modeling and “big data” while others refer to price optimization to mean using information about consumers’ price sensitivity as a rating factor.
For insurers, this definition uncertainty is the problem.
That “lack of consensus” has led regulators to question some pricing techniques that have been used by insurers for decades, according to Robert Hartwig, president of the Insurance Information Institute, in testimony at the National Conference of Insurance Legislators in July.
“The lack of consensus definition has clearly sewn confusion and led states to question substantively different pricing techniques, some of which regulators had approved for decades, potentially leading to unintended and adverse consequences for consumers,” Hartwig said.
“The use of judgment in ratemaking is universally recognized and accepted by regulators and falls within the scope of the actuarial standards of practice,” he said.
Hartwig contends that PO is consistent with actuarial principles, which recognize that business considerations are part of ratemaking and the indicated rate for an insurance product and the market price are different in most circumstances.
Quoting the Casualty Actuarial Society’s (CAS) statement of principles, Hartwig added, “By interacting with professionals from various fields including underwriting, marketing, law, claims, and finance, the actuary has a key role in the ratemaking process.”
Hartwig cited young drivers as an example of where judgment enters into in ratemaking.
“As high as rates are on newly-licensed drivers, those rates would be higher still if companies did not exercise judgment and instead took the full rate indication for this class of driver,” he said. “Regulators have never objected to this pricing behavior, which strays from strict adherence to indicated rates but reflects market realities.”
Florida has its own definition. In its bulletin from May 2015, the Florida Office of Insurance Regulation defined price optimization as: “a process for modifying the insurance premium 鈥� that would otherwise be charged to an insured or class of insureds 鈥� in order to maximize insurer retention, profitability, written premium, market share, or any combination of these while remaining within real world constraints.”
In general, price optimization uses the economic concept of “price elasticity of demand,” which is a measure of the responsiveness of the quantity of a good or service purchased to a change in its price. Advocates of price optimization have pointed to such non-risk-related items as cross-selling opportunities, consumer retention, and conversion rates as potential benefits of the process.
However, according to Florida and other states opposing the practice, “it is possible for an insurer to use price optimization or price elasticity of demand for the purpose of price discrimination, which is when the insurer charges different prices for the same product to different market segments with reduced regard for expected losses and expenses.”
When it is used in this way, price optimization results in rates that are unfairly discriminatory in violation of Florida law, Florida’s regulators say.
Regulators complain that price discrimination can give a bargain to one consumer over another with the same risk.
For example: Consumer A and consumer B are both women of the same age residing in the same state, with similar driving records, credit history, and other factors. Both A and B have bought auto insurance from the same carrier and pay the same premium. But now A is discovered by the carrier to be browsing multiple insurer websites looking for a better deal on her car insurance, prompting the insurer to gives her a 10 percent discount. B is not browsing for a better deal online and gets no such offer.
This difference in treatment is potentially unfair, regulators say.
“We’ve got no problem with an insurer modeling its expected costs related to a particular policyholder on traditional factors like driving history, and then pricing the risk accordingly,” said Lee Barclay, senior actuary at state of Washington’s Office of the Insurance Commissioner, in a May 2015 article on CarrierManagement.com, titled “Price Optimization or Price Discrimination? Regulators Weigh In.”
“But this is modeling a consumer’s behavior insofar as their likelihood of renewing with an insurer,” Barclay added. “When you are modeling things other than the risk that will be transferred from the policyholder to the insurer, we have concerns.”
Voodoo Pricing
Consumers whose premiums change for no apparent reason often turn to their agents for an explanation.
But thus far, the nation’s largest association of independent agents and brokers has remained quiet on the issue of price optimization, although it is keeping its eye on it.
When price optimization results in a policy discount that’s good news from an agency standpoint, but when somebody’s auto insurance takes a premium hike, that makes an agents role more difficult, says Bill Wilson, director of the Virtual University at the Big “I” (Independent Insurance Agents & Brokers of America).
“If somebody has a $200 jump in their premium, they ask the agent why. If they know it’s pricing optimization, how do you tell somebody, ‘Well, the insurance company is going to charge you as much as they possibly can before you move your business,'” Wilson said. “That makes it so tough for an agent.”
He is concerned about the effect on consumer trust in the industry.
“With the disdain that so many consumers have for the industry, I think a lot of them will suspect price optimization is some kind of voodoo pricing system,” Wilson said.
Wilson says data-driven tools in insurance pricing are interesting, but also scary. “Particularly when you’re trying to explain this to consumers that don’t really understand the industry to begin with, and are at best, highly suspicious of what we do, and how we do it.”
Wilson also has concern over the reliability and the accuracy of the data, stemming from his own personal experience. “I’ve had two personal experiences where I’ve had homeowners’ rate hikes that were attributable to credit scoring. Both of them were in error,” he said.
Wilson’s expertise in insurance led him to “pick up on” the mistake and get it fixed. “One was a $1,000 homeowners’ increase, because they had used the wrong credit data. They used it for a different Wilson couple,” he said. The other mistake resulted in a $700 rate increase that also turned out to be a mistake.
Wilson admits that at some point the Big “I” may have to take an official stance on the use of price optimization with independent agency carriers. That is if the issue continues to remain an issue.
Price optimization may end up being a “non-issue if, universally, regulators don’t permit it,” he said. Right now the Big “I” continues to take a “look and see” approach to price optimization.
Wilson says the Big “I” plans to offer a webinar on predictive modeling, data analytics, price optimization, and other issues affecting the pricing structure of various types of insurance in February 2016.
For now, the Big “I'”s Wilson says agents understand the value of data to make risks more predictable, and that helps control the pricing. He hopes the focus will continue to be on risk-based underwriting rather than optimizing prices on non-risk-based factors.
“As long as they don’t factor too much [price optimization] into the rate, it is risk-based,” he said hopefully.
The Big “I” may not be ready to take a position but others are.
According to Frederick Fisher, J.D., of Fisher Consulting Group Inc. in El Segundo, Calif., price optimization is all about how insurers can get more money out of their insureds but they may come to regret going down its path.
“The concept of price optimization is going to get a lot of people in a lot of hot water and, possibly, even give rise to significant underwriting losses, especially in such a competitive environment,” Fisher said, who consults on loss control, provides expert witness work, litigation strategies, and coverage management and review for policyholders.
In his view, insurers do not need price optimization to turn a profit.
Insurance company profitability “still boils down to what’s going to cause the losses, what’s the probability of that taking place, and finally can we set forth a pricing mechanism that’s going to make sense and leave us with a profit,” Fisher said. “What you want to do is stay within the actuarial model. As long as you’re staying within the actuarial model, I’ve never known anybody that didn’t make money 鈥� It all goes back to assessing the risk, which price optimization does not.”
In Fisher’s view, price optimization may not be around for long.
“It’s very clear that the departments of insurance are not going to allow price optimization 鈥� simply because you think you can get more money here versus more money there 鈥� without that being associated with some change in hazard risk,” he said.
Even with varying state definitions of what price optimization is (or is not), Fisher says there’s one thing that’s in common with them all 鈥� and that is that pricing optimization has nothing to do with the risk.
“I don’t know that a reinsurer is going to be too crazy about a price optimization, when there’s no hazard justification behind it,” he said.
Wells is editor-in-chief of 缅北轮奸 magazine, where a version of this article originally appeared.
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