Insurers use underwriting to sort applicants into classes according to age, health, occupation, and other characteristics. From a pricing standpoint, the primary reason for grouping similar exposures together into homogeneous classes is that:

Why C is correct

The law of large numbers enables accurate prediction of aggregate losses only when the exposure units are similar in nature and face approximately the same chance of loss. By classifying risks into homogeneous groups, the actuary can treat each class as a single statistical population and set an adequate, non-discriminatory premium for it. Mixing fundamentally different exposures, such as healthy young workers with seriously ill older applicants, distorts the average and makes losses unpredictable.

Why the other options are wrong

• A) No classification system guarantees profit; pricing adequacy, claims experience, and expenses still govern results, and a class can still produce losses.
• B) Charging every applicant the same premium is community rating, not risk classification; homogeneous classes are used so that each class pays a premium reflecting its own loss potential.
• D) Classification cannot prevent claims from occurring; it only improves the insurer's ability to predict and price the losses that do occur.

Memory hook

Predictability needs sameness. A mixed pool is a muddled forecast.