What is the 'law of large numbers' in insurance?

The 'law of large numbers' is a fundamental principle in insurance that states that as the number of insured risks increases, the actual loss incurred will become more predictable and will align closely with the expected loss. This principle relies on the idea that while individual losses are uncertain, when observing a large group of similar risks, the average outcome becomes more stable and certain.

In practical terms, this allows insurance companies to set premiums based on statistical data, ensuring they can cover claims and remain profitable. By pooling many similar risks, insurers can effectively forecast the overall losses they will face, which aids in financial planning, premium setting, and risk management. This concept highlights the importance of having a large enough sample size in order to make reliable predictions about losses and helps stabilize the insurance market.

The other options do not capture this principle as accurately. While premiums are influenced by risk assessments (as indicated in the first and third options), these do not encapsulate the broader predictive power of the law of large numbers. The sharing of risk among policyholders mentioned in the last option is an aspect of how insurance operates but does not specifically pertain to the statistical underpinning of predicting loss behavior across a large group.