Optimizing Prices in Insurance: The Intersection of Logic, Language, Forms, and Facts

I spend a fair amount of time on LinkedIn posting and reading about insurance. Recently, a discussion emerged regarding Instacart’s decision to discontinue an AI-powered tool that allowed retailers to charge different prices for the same products. The apparent goal was to maximize profits by charging more to those willing to pay higher prices.

This move reportedly followed investigations by at least two consumer organizations, which claimed to have evidence that several well-known retailers were using—or at least testing—AI pricing systems. According to a CBS News report, one investigation revealed that prices could vary by as much as 23% for identical products, depending on the customer.

The LinkedIn discussion raised the question of whether such practices should be legal in the property/casualty insurance industry. Insurance pricing is subject to more stringent regulations than many other sectors, beyond general anti-discriminatory practices that apply across industries (e.g., race, color, creed, national origin, religion, etc.).

“Price optimization” is the term often used to describe the variation in pricing of identical products among different customers. Generally, this term encompasses pricing variations based on individual customer demand, competitive factors, and fluctuating costs, all aimed at maximizing revenue and profits.

Most likely, everyone reading this article has experienced price optimization, either benefiting from it or feeling like a “victim.” While no one enjoys being overcharged, everyone appreciates a good deal. Price optimization has been prevalent in the travel industry for decades, affecting airlines, hotels, and cruise lines.

More recently, it has become common in industries like cable TV, internet, and cell services. For instance, our cable TV provider raises our monthly fee annually. My wife often calls to complain, and the provider usually backs off the increase to prevent us from canceling, sometimes even offering a “new customer” program with better channels or reduced prices. Many customers likely accept the higher price without question. That’s price optimization in action.

With the rapid advancement of data analytics and artificial intelligence, we can expect the use of price optimization algorithms to increase across various industries, unless legislative or regulatory restrictions are imposed.

Turning specifically to the property/casualty insurance industry, Insurance Journal reported as early as November 2014 that Maryland became the first state to declare price optimization illegal. On October 31, 2014, the Maryland Insurance Administration (MIA) issued Bulletin 14-23, stating that the use of “price optimization” violated §27-212(e)(1) of the state insurance code.

The Maryland bulletin broadly defined price optimization as any practice of varying rates or premiums based on factors other than the risk of loss. The MIA referenced a 1997 Maryland Court of Appeals case (Insurance Commissioner v. Engelman, 345 Md. 402, 413) to support its ruling.

One of the most controversial forms of price optimization stems from the economic principle of “price elasticity,” where a provider charges the highest price the market can bear without losing customers. When applied at the individual level, this practice can trigger long-standing state insurance laws that prohibit unfair discrimination in pricing. What constitutes “unfair” is determined by regulatory agencies and the courts.

The Maryland bulletin noted that if an insurer’s analysis indicated that a policyholder was likely to switch to another insurer, that policyholder would be charged a lower premium than one who was deemed unlikely to switch. It also described a price optimization model that considered whether a policyholder had previously complained to the insurer, suggesting that such complaints indicated a lower likelihood of accepting a premium increase. This could lead to unfair discrimination against policyholders with identical risk characteristics.

Less than three months later, Ohio issued a similar bulletin warning insurers against using price optimization that constitutes unfair discrimination. California and New York followed suit shortly thereafter, and by May 2015, Florida became the fifth state to ban discriminatory price optimization. To date, at least 18 states and the District of Columbia have expressly prohibited price optimization as a rating or premium development tool.

Are all property/casualty product premiums based solely on the risk of loss? Probably not. Premiums typically cover insurer costs, including risk-based loss costs and operational expenses. Two insureds with identical risk exposures may incur very different operational costs for the insurer, making it reasonable to account for these differences.

Additionally, many rating plans allow for judgmental credits or debits within specific filed ranges. It’s possible that a credit may be applied to a prospective customer’s premium based more on competitive pressures than on the actual likelihood of them being a profitable policyholder. Risk is not always quantifiable, but insurers should strive for pricing equity that aligns with anti-discriminatory pricing laws.

What do you think? If you’re reading this article online, I encourage you to share your opinions and experiences in the Comments section. If you’re reading in print, visit the online version to join the conversation. You may be surprised by the insights you gain from other Insurance Journal subscribers.

P.S. — If you’re active on LinkedIn, feel free to connect with me. I will be posting and commenting more frequently this year, including discussions on this topic with links to additional resources.

Wilson, CPCU, ARM, AIM, AAM is the founder and CEO of InsuranceCommentary.com and the author of six books, including “When Words Collide…Resolving Insurance Coverage and Claims Disputes,” which BookAuthority ranks as the #1 insurance book of all time. Email: Bill@InsuranceCommentary.com.

I spend a fair amount of time on LinkedIn posting and reading about insurance. Recently, a discussion emerged regarding Instacart’s decision to discontinue an AI-powered tool that allowed retailers to charge different prices for the same products. The apparent goal was to maximize profits by charging more to those willing to pay higher prices.

This move reportedly followed investigations by at least two consumer organizations, which claimed to have evidence that several well-known retailers were using—or at least testing—AI pricing systems. According to a CBS News report, one investigation revealed that prices could vary by as much as 23% for identical products, depending on the customer.

The LinkedIn discussion raised the question of whether such practices should be legal in the property/casualty insurance industry. Insurance pricing is subject to more stringent regulations than many other sectors, beyond general anti-discriminatory practices that apply across industries (e.g., race, color, creed, national origin, religion, etc.).

“Price optimization” is the term often used to describe the variation in pricing of identical products among different customers. Generally, this term encompasses pricing variations based on individual customer demand, competitive factors, and fluctuating costs, all aimed at maximizing revenue and profits.

Most likely, everyone reading this article has experienced price optimization, either benefiting from it or feeling like a “victim.” While no one enjoys being overcharged, everyone appreciates a good deal. Price optimization has been prevalent in the travel industry for decades, affecting airlines, hotels, and cruise lines.

More recently, it has become common in industries like cable TV, internet, and cell services. For instance, our cable TV provider raises our monthly fee annually. My wife often calls to complain, and the provider usually backs off the increase to prevent us from canceling, sometimes even offering a “new customer” program with better channels or reduced prices. Many customers likely accept the higher price without question. That’s price optimization in action.

With the rapid advancement of data analytics and artificial intelligence, we can expect the use of price optimization algorithms to increase across various industries, unless legislative or regulatory restrictions are imposed.

Turning specifically to the property/casualty insurance industry, Insurance Journal reported as early as November 2014 that Maryland became the first state to declare price optimization illegal. On October 31, 2014, the Maryland Insurance Administration (MIA) issued Bulletin 14-23, stating that the use of “price optimization” violated §27-212(e)(1) of the state insurance code.

The Maryland bulletin broadly defined price optimization as any practice of varying rates or premiums based on factors other than the risk of loss. The MIA referenced a 1997 Maryland Court of Appeals case (Insurance Commissioner v. Engelman, 345 Md. 402, 413) to support its ruling.

One of the most controversial forms of price optimization stems from the economic principle of “price elasticity,” where a provider charges the highest price the market can bear without losing customers. When applied at the individual level, this practice can trigger long-standing state insurance laws that prohibit unfair discrimination in pricing. What constitutes “unfair” is determined by regulatory agencies and the courts.

The Maryland bulletin noted that if an insurer’s analysis indicated that a policyholder was likely to switch to another insurer, that policyholder would be charged a lower premium than one who was deemed unlikely to switch. It also described a price optimization model that considered whether a policyholder had previously complained to the insurer, suggesting that such complaints indicated a lower likelihood of accepting a premium increase. This could lead to unfair discrimination against policyholders with identical risk characteristics.

Less than three months later, Ohio issued a similar bulletin warning insurers against using price optimization that constitutes unfair discrimination. California and New York followed suit shortly thereafter, and by May 2015, Florida became the fifth state to ban discriminatory price optimization. To date, at least 18 states and the District of Columbia have expressly prohibited price optimization as a rating or premium development tool.

Are all property/casualty product premiums based solely on the risk of loss? Probably not. Premiums typically cover insurer costs, including risk-based loss costs and operational expenses. Two insureds with identical risk exposures may incur very different operational costs for the insurer, making it reasonable to account for these differences.

Additionally, many rating plans allow for judgmental credits or debits within specific filed ranges. It’s possible that a credit may be applied to a prospective customer’s premium based more on competitive pressures than on the actual likelihood of them being a profitable policyholder. Risk is not always quantifiable, but insurers should strive for pricing equity that aligns with anti-discriminatory pricing laws.

What do you think? If you’re reading this article online, I encourage you to share your opinions and experiences in the Comments section. If you’re reading in print, visit the online version to join the conversation. You may be surprised by the insights you gain from other Insurance Journal subscribers.

P.S. — If you’re active on LinkedIn, feel free to connect with me. I will be posting and commenting more frequently this year, including discussions on this topic with links to additional resources.

Wilson, CPCU, ARM, AIM, AAM is the founder and CEO of InsuranceCommentary.com and the author of six books, including “When Words Collide…Resolving Insurance Coverage and Claims Disputes,” which BookAuthority ranks as the #1 insurance book of all time. Email: Bill@InsuranceCommentary.com.