What principle does the 'law of large numbers' illustrate in insurance?

The 'law of large numbers' is fundamental to the functioning of insurance, emphasizing that as the number of exposure units increases, the actual loss experience will more closely approximate the expected loss. This principle indicates that insurers can predict losses more accurately over a large group of similar risks due to the statistical nature of probability.

In practice, when a large enough sample of risks is covered, the random fluctuations in loss that may occur in smaller groups tend to balance out, leading to a more stable and predictable outcome regarding loss ratios. This predictability allows insurers to set premiums more reliably and to determine reserves that are adequate to cover future claims. As the volume of insured risks increases, individual outcomes (i.e., claims) become less significant, enabling insurers to make informed decisions based on statistical data rather than isolated incidents.

This concept is foundational for underwriting and pricing insurance policies, as it informs how insurers assess risk and manage their portfolios effectively. Therefore, the predictability of loss ratios indeed increases with a larger number of risks, making the correct choice a clear reflection of the underlying principle described by the law of large numbers.