American Property Casualty Insurance White Paper Demonstrates Fairness & Accuracy of Auto Insurance Factors and the Cost of Claims

CHICAGO, Illinois, Nov. 2 (TNSRes) -- The American Property Casualty Insurance Association issued the following news release on Nov. 1, 2021:

Today the American Property Casualty Insurance Association (APCIA) released an important new white paper, "Behavioral Validation of Auto Insurance Rating Variables," which highlights how many of the most commonly used driving rating factors are related to driving behaviors and the risk of loss.

Since the first auto insurance policies were sold nearly 125 years and trillions of miles ago, auto insurance pricing and underwriting has continuously evolved to reflect the countless innovations in motor vehicle technology and design, as well as changes in road and traffic management infrastructure, driving patterns, and behaviors. These innovations continue to evolve today, and telematics, which allow insurers to collect driver behaviors in real time, represents one of the more recent developments in automobile insurance underwriting and pricing.

"Because the population of drivers is large and diverse, the best--and fairest--way to manage the inherent complexity and uncertainty associated with auto insurance pricing is to use a large combination of actuarially sound and independently predictive rating variables," said Robert Hartwig, author of the paper and Clinical Associate Professor of Finance, Risk Management & Insurance at the Darla Moore School of Business, University of South Carolina.

The most commonly used rating factors have been in use for decades because they have been proven to be highly predictive of future losses. These factors fall into four major categories: (i) policy attributes, (ii) driver characteristics, (iii) driving environment and (iv) vehicle characteristics. Several commonly used variables within each category are shown in the table below:

Figure 1. Categorization of Rating Variables Commonly Used by Auto Insurers

Category Example Variables

Policy Attributes Number of Drivers, Number of Vehicles, Limits, Deductibles, Prior Lapse, Coverages

Driver Characteristics Age, Gender, Credit Behavior, Marital Status, Occupation, Education, Driving Record, Moving Violations, Claims History, Miles Driven

Driving Environment Territory (Location), Garaged or Street Parked, Repair Costs, Medical Costs, Weather Exposures

Vehicle Characteristics Vehicle Age/Make/Model

"Insurers want to make the most accurate risk assessment of each driver and the use of highly accurate and predictive data helps to achieve that goal," said Robert Gordon, senior vice president, policy, research, and international for the American Property Casualty Insurance Association (APCIA). "If you are a low-risk driver, you shouldn't have to subsidize insurance rates for high-risk drivers. Those who pose a greater statistical risk should pay more than those who pose a smaller risk.

"Additionally," he continued, "we want our products to be affordable and accessible to the largest possible number of people. That starts by doing what's fair, which is using a large combination of accurate variables that help predict risk. In this way, insurers maximize pricing accuracy and assure that no single rating variable has a disproportionate impact on an individual's premium. This approach to pricing also allows insurers to offer their products to a broader range of consumers and to include incentives that promote safe and responsible driving behaviors."

Actuarial standards of practice and state insurance regulators also mandate that all rating factors used by insurers comply with stringent requirements documenting strong statistical correlations between rating variables and loss outcomes.

While most people have an intuitive understanding of why most variables are used to develop auto insurance rates, there are also some less intuitive variables that have proven to be highly accurate.

No matter how intuitive a variable is to the consumer, they all must be strong predictors of future loss.

"While accident history seems very intuitive in the determination of what someone pays for insurance, only looking at accident history provides a very limited view of how careful or risky a driver may be," said Gordon. "Rather than just relying on state motor vehicle records, which are plagued by many omissions and unreported events, insurers use a wide variety of factors that predict the likelihood of someone having an accident or filing a claim to develop a more complete picture of a driver."

Numerous studies show that there are many commonly used factors that are far more reflective of driver safety than just one's own driving record, as records can be wiped clean of injurious car accidents and safety violations through the completion of easy driver safety courses. Most consumers benefit from the factors insurers currently use as they help keep rates affordable for drivers less likely to have an accident.

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White Paper's "Behavioral Validation of Auto Insurance Rating Variables (https://www.apci.org/attachment/static/5162) Key Findings:

In recent years, some insurers have turned to telematics to gather data about actual driver behaviors in real time, rather than relying on data after-the-fact from accident reports or from driver surveys. While telematics cannot monitor every risky driving behavior, the technology can capture data on several specific behaviors that allow insurers, drivers, and regulators to observe direct linkages between the effects of these behaviors on claim costs and the impact on premiums.

This white paper uses telematics and claims data to demonstrate strong correlations between several commonly used driver-related characteristics and losses.

Population Density is an important variable

Telematics data prove that population density is a highly accurate predictor of insurance cost. Population density can be viewed as a variable that reflects territory, such as urban vs. rural driving. Drivers in areas where the population density is the highest are associated with insurance claim costs that are approximately 20 percent higher than the overall population of drivers. Conversely, drivers in areas with the least population density tend to have claim costs that are 20 percent below the overall population of drivers. Telematics data also demonstrates that drivers in areas with a high population density are far more likely to engage in hard braking behaviors. Frequent hard braking and hard acceleration have been proven to result in more frequent accidents.

Education

Education is another driver characteristic that is highly predictive of loss.

The association between claim costs and education can once again be demonstrated using telematics data. Drivers with lower levels of education attainment are associated with insurance claim costs that are approximately 5 to 10 percent higher than the overall population of drivers. Conversely, drivers with higher educational attainment have claim costs that are at least 5 to nearly 20 percent below the overall population of drivers.

Telematics data shows there is a very clear association between educational attainment and hard braking behavior. Drivers with high degrees of educational attainment engage in hard braking behaviors at least 5 percent less frequently than the overall population of drivers, while those with lower educational attainment engage in hard braking approximately 5 percent more frequently than the overall driver population.

Hard acceleration is another risky driving behavior that is correlated with education and loss. Specifically, lower levels of educational attainment are associated with an increased frequency of hard acceleration behaviors which ultimately lead to higher relative costs.

Occupation

Occupation has also been found to be highly predictive of loss.

Telematics data also confirms the association between claim costs and occupation.

Drivers with certain occupations are associated with approximately 5 to 10 percent higher insurance claims costs than the overall population of drivers. Conversely, drivers with other occupations have claim costs that are 5 to 10 percent below the overall population of drivers.

Employing telematics data demonstrates a very clear association between certain occupations and hard braking behavior. Occupation is predictive because it's about vehicle usage and the regularity of commutes. For example, realtors generally drive more frequently and often to areas they aren't as familiar with compared to a schoolteacher who tends to drive the same route to and from school every day. It is important to note that the occupational risk groups are not based on income--they are based on actual claim costs. Low-income and high-income occupations exist within each risk group.

Additionally, U.S. Census data shows that each risk group is represented by people from every race and ethnicity. For example, data from one insurer shows that:

* Bank tellers have lower claim costs compared to psychologists

* Teachers have lower claim costs compared to financial advisors

* Enlisted military have lower claim costs compared to pharmacists

* Firefighters have lower claim costs compared to dentists

* Receptionists have lower claim costs compared to veterinarians

Marital Status

Marital status is highly predictive of loss.

Married drivers are associated with insurance claim costs that are approximately 20 percent lower than the overall population of drivers. Conversely, single drivers have claim costs that are approximately 15 percent above that of the overall driver population.

Telematics data also demonstrates a very clear association between marital status and hard braking behavior. Married drivers engage in hard braking behaviors approximately 10 to 15 percent less frequently than the overall population of drivers.

The telematics data therefore demonstrate that marital status is strongly predictive of auto insurance claims costs, in part because hard braking behavior is far more prevalent among single drivers than married drivers.

Credit Based Insurance Scores

Credit based insurance scores (CBIS) first began being used approximately 20 years ago because they are highly predictive of loss.

Insurer use of CBIS has been extensively studied by insurers, regulators, the federal government, and academics. Every serious and reputable actuarial study on the issue, including a seminal study in 2007 by the Federal Trade Commission (FTC), has reached the same conclusion: there is a high correlation between insurance scores and the likelihood of insurance claims.

Telematics data shows that hard braking behaviors are strongly associated with credit-based insurance scores and higher risk drivers. The cost to insure the highest risk drivers, those with the lowest credit-based insurance scores is approximately 28 percent higher than for the overall driver population. Conversely, the cost to insure drivers with the strongest CBIS is about 30 percent less than the overall driver population. The telematics data also demonstrates that credit-based insurance scores are strongly predictive of auto insurance claims costs. These findings have been corroborated in many independent studies over the past two decades--conducted by numerous state insurance departments, academics, and the federal government.