An insurer may use which principle to predict future losses based on past data?

The Law of Large Numbers is a fundamental principle in insurance that allows insurers to predict future losses based on past data. This statistical concept suggests that as the size of a sample increases, its average will get closer to the expected value. In the context of insurance, when an insurer collects data from a large pool of similar risks, they can more accurately estimate the likelihood of future claims and losses.

This principle is crucial because it provides a foundation for underwriting and risk assessment. Insurers rely on historical data from a broad range of policyholders to determine premium rates and reserve funds needed for expected future claims. The more data the insurer has, the more reliable its predictions become, allowing for improved financial stability and risk management.

In contrast, while risk assessment involves evaluating potential risks, it does not specifically predict losses based on historical data. Behavioral finance addresses psychological influences on investors and economists, which is less relevant to the core actuarial practices of predicting losses. Cost-benefit analysis, although important in many business contexts, does not focus directly on using past loss data to forecast future occurrences. Thus, the Law of Large Numbers stands out as the principle most pertinent to predicting future losses through historical data analysis.