Most applicants are generally aware of how their life insurance premiums are calculated, in that the premiums reflect variables like age, health status, lifestyle, etc. However, very few people understand the actual risk assessment process that life insurers go through when evaluating applicants for coverage. An applicant’s information is assessed and classified based on algorithmically-based insurance tables. The applicant’s premiums directly depend on his/her classification according to these tables. To understand the risk assessment process, though, you must first understand how insurers formulate the tables and determine the exact premium the insured must pay. In this post, we will offer a window into the life insurance risk assessment process and how it affects premiums.
Quantifying Risk
Insurers define risk as the likelihood of loss. When you purchase an insurance policy, the risk of loss is transferred from you to your insurance company. To make the transfer of risk a profitable endeavor, insurers have to figure out the number of losses that will occur. However, this is impossible to do on an individual basis, so insurers instead rely on the statistical principle of the law of large numbers. In doing so, they are able to predict fairly accurately how many losses will actually occur within a group of individuals.
The Law of Large Numbers
A life insurer has no way of determining which individual policyholders will die, but as the sample or group becomes larger, the predictions become more and more accurate. With statistics, the insurer can accurately predict the number of policyholders that will pass away within a large group. This is where the law of large numbers comes into play: the bigger the group, the more accurate the prediction of future losses over a certain period of time will be. A good example of the law of large numbers if the flipping of a fair coin. If you flip the coin only two or three times, you will likely get skewed results that inaccurately favor either heads or tails. However, as the number of trials increases, the frequency of heads or tails will get closer and closer to 50%. The law of large numbers is critical in the insurance industry because it guarantees consistent results over the long term for otherwise random events.
Exposure Units
With insurance, an exposure unit is the piece of property or individual that is insured. For the law of large numbers to work, insurers must combine a large amount of homogeneous exposure units. Applied to life insurance, the exposure unit is the monetary value of the policyholder’s life. As the number of exposure units in a group increases, the amount of error in predicting losses drops. Insurance companies focus only on average risk because, in doing so, the extremes of the loss spectrum cancel each other out.
Actuaries
Actuaries are mathematicians who collect and evaluate statistical data related to risk and exposure units. It is this data that is the basis for the formulation of life insurers’ morbidity (sickness) and mortality (death) tables. Using these tables, actuaries predict the likelihood of future losses resulting from death and illness. The tables factor in many variables that can raise or lower the loss risk, such as age, medical history, tobacco use, etc. Each applicant is classified and his/her premiums are determined according to where his/her profile falls based on the tables.
Where Premiums Come From
So how does the risk assessment process finally translate into the premiums that an applicant will pay? Basically, premiums are what a life insurance company charges in order to provide for expenses, profits, and the cost of anticipated losses. The amount of loss expected is based on the insurer’s past experience with average risk. Of course, some applicants will far outlive their life expectancy, and thus will pay premiums for a much longer time than expected. However, for every applicant like that, there is also one who will die prematurely after paying premiums for only a very short period of time. Consequently, both the high and low extremes negate each other, which leaves the average risk as the basis for determining expected losses.
Taken from : http://www.lifeinsurancerates.com/risk-assessment-revealed.html
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