What principle relies upon the predictability of loss based on reviewing loss experiences for large numbers of similar risk entities?

The Law of Large Numbers is a fundamental principle in insurance that asserts that as the number of similar risk entities increases, the actual loss experience will tend to approximate the expected loss experience. This principle relies on the idea that large groups of similar risks can be analyzed to predict future losses with greater accuracy. Insurers utilize this principle to determine premium rates and assess risks, ensuring that they can cover the claims expenses that might arise for those risks.

By analyzing large data sets of insured individuals with similar risk profiles, insurance companies can effectively predict the likelihood and cost of losses. For example, if an insurer observes that a certain percentage of policyholders typically file claims in a given year, it can rely on that statistic to set premiums for future policyholders with similar characteristics. This predictive capability becomes more reliable as the data set grows larger, thus allowing the insurer to manage its exposure to risk more effectively while maintaining profitability.