According to the law of large numbers, how does increasing the number of similar insured units affect prediction of losses?

The law of large numbers is a fundamental principle in insurance and probability that states as the number of similar insured units increases, the actual results (losses) will more closely reflect the expected results (predictions based on probability). This principle relies on the idea that while individual outcomes may vary widely, the average of those outcomes will stabilize as more units are considered.

Increasing the number of similar insured units allows insurers to pool risks effectively. With more data points, underwriters can calculate averages and make more accurate predictions about total losses. Essentially, the variability surrounding the prediction diminishes because the random fluctuations in losses have a tendency to cancel each other out over a larger pool. Thus, actuaries can better estimate future claims and set premiums accordingly, making the overall risk profile more predictable.

This understanding is crucial for insurers when determining appropriate reserves and pricing policies, ensuring that they remain financially viable while adequately covering their clients.