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Home / Insurance / Triple-I Blog | The Illinois Bill highlights the need for education on risk-based pricing of insurance coverage

Triple-I Blog | The Illinois Bill highlights the need for education on risk-based pricing of insurance coverage

Legislation under consideration in Illinois underscores the need for lawmakers and other policymakers to be better educated about the importance of risk-based pricing and how it works.

The Motor Vehicle Insurance Fairness Act would prevent insurers from considering non-driving factors, such as credit scores, when setting premium rates. The bans include factors that actuaries have shown strongly correlate with the likelihood that a driver will eventually file a claim, as well as those that insurers are already prohibited from using.

This suggests a lack of understanding of risk-based pricing that is not isolated to Illinois lawmakers — indeed, similar proposals are filed from time to time at the state and federal levels.

Confusion is understandable

Risk-based pricing means offering different prices for the same coverage, based on risk factors specific to the insured person or property. If the policy wasn’t priced this way, lower-risk drivers would subsidize riskier ones. Charging higher premiums to higher-risk policyholders helps insurers underwrite a wider range of policies, improving both the availability and affordability of insurance.

The concept becomes complicated when actuarially sound rating factors intersect with other attributes in ways that could be perceived as unfairly discriminatory. For example, concerns are being raised about the use of credit-based insurance scores, geography, home ownership and motor vehicle records in determining home and auto insurance premiums. Critics say this could lead to “proxy discrimination,” where people of color in urban areas are charged more than their suburban neighbors for the same coverage.

Confusion is understandable given the complex models used to assess and price risk. To navigate this complexity, insurers hire actuaries and data scientists to quantify and differentiate between a range of risk variables while avoiding unfair discrimination.

Appropriate protections are in place

It is important to remember that insurance companies do not make money not insure people. They deal with pricing, underwriting and risk taking.

Because of the critical role insurance companies play in facilitating commerce and protecting the lives and property of individuals, insurance is one of the most regulated industries on the planet. To ensure that sufficient funds are available to pay claims, regulators require insurance companies to maintain a cushion known as policyholder excess.

Rating agencies, such as Standard & Poor’s and AM Best, expect insurers to have surpluses in excess of what regulators require to maintain their financial strength. Strong financial strength enables insurers to borrow money at favorable interest rates – further promoting insurance availability and affordability.

In addition to these restrictions, state regulators have the authority to limit the rates that insurers can charge within their jurisdictions.

No profit, no insurance companies – no insurance companies, no coverage

Like any other business, insurance companies must make a reasonable profit to remain solvent. Because they can’t just move money around like more lightly regulated industries can, the only way to generate insurance profits is through rigorous pricing and expense and loss controls. Insurance companies don’t want to overcharge and send consumers shopping for a better price, or underprice and experience losses that erode their ability to pay claims.

In this context, it is important to note that private auto and homeowner insurance premiums have remained relatively flat as inflation and claims costs have soared through the pandemic and supply chain issues related to Russia’s invasion of Ukraine (see diagram below).

During this period, writers of these coverages have struggled to make an insurance profit. Passenger cars have been a primary driver of the overall industry’s weak insurance performance. Dale Porfilio, Triple-I’s director of underwriting, recently said that the 2022 total cost of personal auto insurance is forecast at 111.8, 10.4 points worse than 2021 and 19.3 points worse than 2020. Combined ratio represents the difference between payments and costs paid and premiums. collected by insurance companies. A combined ratio below 100 represents an insurance gain and one above 100 represents a loss.

Even as inflation moderates, loss trends in both of these lines—associated with increased accident rates and severity in auto and extreme weather trends in homeowners and autos—will require premium rates to rise. The question is: Will the cost fall evenly across all policyholders, or will rates more accurately reflect policyholders’ risk characteristics?

Protected classes

The United States recognizes “protected classes”—groups that share common characteristics and for whom federal or state laws prohibit discrimination based on those characteristics. Race, religion, and national origin are most commonly referred to when describing protected classes in the context of insurance ratings, and insurers generally do not collect information on these “big three” classes. Any discrimination based on these attributes must arise from the use of data that can act as proxies for protected classes.

Algorithms and machine learning hold great promise for ensuring fair pricing, but research shows that these tools can reinforce implicit biases.

The insurance industry has been responsive to such concerns. For example, recent Colorado legislation requires insurers to demonstrate that their use of external data and complex algorithms does not discriminate against protected classes, and the American Academy of Actuaries has offered extensive guidance to the state’s insurance commissioner on implementation. The Casualty Actuarial Society also recently published a series of articles (see links on end of post) on the subject.

Correlation matters

Certain demographic factors have been shown to correlate with increased risk of filing a claim. Gender and age strongly correlate with crash involvement, as the National Highway Traffic Safety Administration (NHTSA) data illustrated at right shows.

Similarly, the National Association of Insurance Commissioners (NAIC) data below clearly shows that higher credit scores correlate strongly with lower crash claims.

Similar correlations can be shown for other rating factors. It is important to remember that no single factor is decisive – many are used to assess a policyholder’s level of risk.

Consumers “get it” – when it’s explained to them

A recent study by the Insurance Research Council (IRC) found that consumer skepticism about the relationship between credit history and future insurance claims appears to decrease when the predictive power of credit-based insurance scores is explained to them. Through an online survey of more than 7,000 respondents, the IRC found that:

  • Almost everyone believes that maintaining a good credit history is important, and most believe that it would be “very” or “somewhat” easy to improve their credit;
  • Consumers see the connection between credit history and future payment of bills but are less certain about the connection between credit history and future insurance claims.
  • After reading that many studies have shown its predictive power, most people agree with using credit-based insurance scores to rate insurance, especially for drivers with good credit who could benefit from it.

If consumers “get it” when you share data with them, maybe politicians and lawmakers can too.

Read more:

Triple-I Issues Briefs

Risk-based pricing of insurance

Breed and insurance rates

Personal car insurance rates

Drivers for homeowners insurance are increasing

How inflation affects P/C insurance premiums – and how it doesn’t

The Triple-I Blog

Inflation trends shine a little for P&C, but insurance profits still elude most lines

Education can overcome doubts about credit-based insurance outcomes, IRC survey suggests

Matching price to risk helps keep insurance available and affordable

Clarifying concerns about race in insurance pricing

Delaware Legislature adjourns without action to ban gender as auto insurance factor

Triple-I: Rating Factor Variation Drives Auto Insurance Rating Accuracy

Car insurance assessment factors explained

The damage actuarial association

• Definition of discrimination in insurance

• Methods for quantifying discriminatory effects on protected classes in insurance

• Understanding Potential Influences of Racial Bias on P&C Insurance: Four Rating Factors Examined

• Approaches to Addressing Racial Bias in Financial Services: Lessons for the Insurance Industry

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