What principle in insurance suggests that larger groups of risks improve loss prediction accuracy?

The principle that suggests that larger groups of risks improve loss prediction accuracy is known as the Law of Large Numbers. This statistical concept indicates that as the number of exposures (or risks) increases, the actual outcomes will tend to converge closer to the expected outcomes due to the averaging effect. In insurance, this means that insurers can predict losses more accurately when they have a large enough pool of policyholders.

The reason this principle is vital to the insurance industry is that it allows insurers to set premiums based on statistical averages rather than on individual risk alone. This enhances the overall stability and predictability of the insurer’s financial outcomes, helping to ensure they have sufficient funds to cover claims.

Other related concepts, such as the Principle of Mutuality or the Rule of Independence, do not specifically address the relationship between group size and prediction accuracy in risk management the way the Law of Large Numbers does. These terms may touch upon other aspects of insurance, such as shared risk among members or the behavior of independent events, but they do not directly capture the essence of improving accuracy through larger groupings, as the Law of Large Numbers does.