For example, within a large group of automobile insurance policyholders, some drivers may have good driving records and some bad. When insurers sort and group the different types of drivers into risk pools of similarly situated individuals, they can estimate expected costs. For a low-risk group, expected costs are low and the insurance company can charge an appropriately low premium. For a high-risk group, the premium is larger because there is greater chance that more claims will be filed, which increases insurers' expected costs. But that is a very simple example.
Risk classification is more than just looking at a driving record.
It is also based on other demographic variables such as age, gender and the value of the car insured, all of which can effect future outcomes. By analyzing these variables and driving characteristics of a large population over time, insurers can better predict risk and reduce uncertainty over prospective claims. This in turn helps consumers by ensuring more fairness and accuracy in the premiums they pay. Risk classification benefits consumers. Since risk classification will always be part science and part art, each risk pool is bound to include policyholders that represent less risk than others in their pool.