What does the "law of large numbers" imply in insurance?

The law of large numbers is a fundamental concept in insurance that states that as the size of a sample increases, the observed outcomes will converge toward the expected outcomes. This principle implies that the larger the number of insured individuals or entities, the more predictable the total losses will be.

In the context of insurance, when a company has more policyholders, it can utilize statistical data to better estimate the frequency and severity of claims. This predictability allows insurers to set premiums accurately and manage their risk effectively. Essentially, with a larger pool of insureds, insurers can expect a more stable and consistent loss experience over time, which helps them to remain solvent and fulfill their financial obligations to policyholders.

The other options do not align with the principles underlying the law of large numbers. For instance, suggesting that larger numbers lead to less predictability contradicts the essence of this statistical law, while the idea that it only applies to high-risk insurance policies is overly restrictive and ignores its broader application across various types of insurance. Lastly, the notion that claims should be evenly distributed among insurers does not capture the variability in risk and claim distribution that insurers must manage.