Georgia Property & Casualty Practice Exam

What principle explains the increased accuracy in loss prediction as exposure units increase?

1. Pure risk

2. Speculative risk

3. Adverse selection

4. Law of large numbers

The correct answer is: Law of large numbers

The principle that explains the increased accuracy in loss prediction as exposure units increase is known as the Law of Large Numbers. This concept states that as the number of exposure units (which could be policyholders, insured items, etc.) increases, the actual loss experience will tend to converge to the expected loss. In simpler terms, when there are many similar exposures, the random fluctuations in loss can be averaged out, leading to more reliable and predictable outcomes. This is especially important in insurance, where risk is pooled. When insurers underwrite a larger group of similar risks, they can better estimate the overall risk and set premiums more accurately, resulting in improved financial stability for the insurance company. The other concepts, while relevant in the realm of risk management and insurance, do not specifically address the relationship between the number of exposure units and the accuracy of loss prediction. Pure risk relates to risks that can only result in a loss, speculative risk involves potential for gain or loss, and adverse selection refers to the tendency of higher-risk individuals to seek more insurance coverage, which can complicate loss prediction rather than clarify it. Thus, the Law of Large Numbers is the key principle that enhances the precision of loss predictions in relation to the number of exposure units involved.