What principle allows insurers to predict claims more accurately?

The principle that allows insurers to predict claims more accurately is the Law of Large Numbers. This principle states that as the size of the sample increases, the actual results will tend to get closer to the expected results. In the context of insurance, this means that the more policies an insurer issues, the more accurately they can estimate future claims based on historical data.

When insurers pool a large number of similar risks, they can calculate probabilities regarding losses with greater precision. For instance, if an insurer has a large database of claims history, they can analyze patterns to forecast future claims more reliably. This helps them in setting premiums that reflect the anticipated risk, ultimately aiding in maintaining the financial stability of the insurance organization.

In contrast, while the Law of Averages might seem relevant, it is more of a general observation rather than a principle designed specifically for statistical prediction and does not provide the same level of accuracy. The Law of Diminishing Returns pertains to economic theory about output and productivity rather than insurance claims. Law of Risk Assessment refers to evaluating risks but does not specifically relate to the statistical basis that the Law of Large Numbers provides for predicting outcomes based on actual experience.