What principle indicates that loss predictions become more accurate with increased exposure?

The principle that indicates loss predictions become more accurate with increased exposure is the Law of Large Numbers. This concept is fundamental in statistics and insurance and asserts that as the number of trials or observations increases, the average of the results will converge towards the expected value.

In the context of insurance, this means that when an insurance company has a larger pool of policyholders, it can better predict the likelihood of claims and losses based on historical data. With more data points, insurers can more accurately calculate the risk associated with a specific type of coverage, which enables them to set appropriate premiums and maintain financial stability.

In contrast, the other principles mentioned do not address the relationship between exposure and accuracy in predictions. Pure risk relates to situations that involve only the possibility of loss, without any chance of gain. Speculative risk involves situations that can result in either a gain or a loss, therefore requiring different assessment techniques. Adverse selection describes the scenario where individuals with a higher risk of loss are more likely to seek insurance, resulting in unbalanced risk pools, but does not directly pertain to the prediction accuracy derived from larger data sets.