What is the main focus of the law of large numbers in insurance?

The law of large numbers is a fundamental principle in insurance that centers around the idea of understanding and managing risk through the aggregation of similar events or exposures. This principle posits that as the number of exposure units increases, the observed outcomes (e.g., claims or losses) will converge to the expected outcomes.

In the context of insurance, this means that by collecting data from a large number of similar risks, insurers can better predict the overall loss experience of their policies. This predictive capability helps insurance companies to set premiums more accurately, manage their reserves properly, and ensure that they can cover future claims. This understanding of similar risk patterns is crucial for maintaining a stable and sustainable insurance model.

The incorrect options center around concepts that, while important in their own right, do not encapsulate the essence of the law of large numbers as directly as understanding similar risk patterns does. For instance, determining premium rates and avoiding insolvency are outcomes or applications derived from the understanding of risks provided by the law of large numbers, rather than the focus of the law itself. Similarly, predicting individual outcomes is less relevant in the context of the law of large numbers, which provides insights at a group level rather than focusing on single instances.