What is the Law of Large Numbers primarily concerned with in insurance?

The Law of Large Numbers is a fundamental principle in insurance that deals with predicting losses based on a large pool of similar risks. This law states that as the number of exposure units increases, the actual losses will tend to converge toward the expected average losses. This principle allows insurers to estimate future claims with greater accuracy, as the unpredictability of individual risks is diminished when viewed collectively across a larger group.

By utilizing this law, insurance companies can set premiums that reflect the anticipated losses, allowing them to manage risk more effectively. This statistical concept underlies the effectiveness of large-scale risk assessment and is critical to the sustainability of the insurance model, as it provides a method to predict financial outcomes based on historical data.

Other options do not capture the essence of the Law of Large Numbers. For example, covering individual high-risk policies does not leverage the statistical benefits derived from large groups. Ensuring a single large claim occurs misinterprets the focus on collective data rather than individual instances. Finally, minimizing the number of valid claims conflicts with the fundamental nature of insurance, which is to protect against losses and honor claims as they arise.