What does the law of large numbers indicate in insurance?

The law of large numbers is a fundamental principle in the field of probability and statistics, particularly in insurance. It states that as the size of a sample increases, the average of the sample approaches the expected value, or the mean, of the population from which the sample is drawn. In the context of insurance, this means that when a large number of similar events or risks are pooled together, the insurer can more accurately predict the total number of claims that will arise and the costs associated with those claims.

This increased accuracy in statistical predictions allows insurers to set premiums more effectively, manage risk better, and ensure that they have sufficient reserves to cover potential claims. By relying on a larger data set, insurers can smooth out anomalies that may occur in smaller samples, leading to more reliable forecasts of future losses.

The other options do not accurately reflect the primary outcome of the law of large numbers. For instance, while it might seem intuitive to think that it reduces claims or operational costs, these outcomes are not direct implications of this law. Rather, they may arise as a consequence of better risk management strategies based on enhanced predictions. Similarly, the law does not directly affect the value of claims themselves; instead, it is concerned with the prediction and management of risk over time