The law of large numbers enables an insurer to?

The law of large numbers is a key principle in the field of insurance that allows insurers to predict losses with greater accuracy. This principle states that as the number of exposure units (such as insured individuals or policies) increases, the actual loss experience will more closely approximate the expected loss experience.

In practical terms, this means that insurers can use historical data from large groups of similar insured risks to forecast future losses. For instance, if an insurer has a large pool of policyholders, statistical analysis can reveal trends and patterns that help determine the average number of claims and the anticipated cost of those claims. As a result, this ability to predict losses with greater precision is crucial for the insurer's financial stability and helps in setting appropriate premium levels to cover those expected losses.

The other options do not directly correlate with the fundamental purpose of the law of large numbers. Minimizing insurance claims and managing investment risk are influenced by various factors, but they are not directly achieved through this statistical principle. The concept of increasing premiums may result from loss predictions, but it is more a consequence of the predictions rather than a direct function of the law itself.