Credibility theory

Credibility theory is a branch of actuarial science. It was developed originally as a method to calculate the risk premium by combining the individual risk experience with the class risk experience.

When an insurance company calculates the premium it will charge, it divides the policy holders into groups. For example it might divide motorists by age, sex, and type of car; a young man driving a fast car being considered a high risk, and an old woman driving a small car being considered a low risk. The division is made balancing the two requirements that the risks in each group are sufficiently similar and the group sufficiently large that a meaningful statistical analysis of the claims experience can be done to calculate the premium. This compromise means that none of the groups contains only identical risks. The problem is then to devise a way of combining the experience of the group with the experience of the individual risk the better to calculate the premium. Credibility theory provides a solution to this problem.

For actuaries, it is important to know credibility theory in order to calculate a premium for a group of insurance contracts. The goal is to set up an experience rating system to determine next year's premium, taking into account not only the individual experience with the group, but also the collective experience.

There are two extreme positions: One is to charge the same premium to everyone, estimated by the overall mean $\overline{X}$ of the data. This makes sense only if the portfolio is homogeneous, which means that all risks cells have identical mean claims. However, if the portfolio is not homogeneous, it is not a good idea to charge premium in this way, since the "good" risks will take their business elsewhere (overcharging "good" people and undercharging "bad" risk people), leaving the insurer with only bad risks. This is an example of adverse selection.

The other way around is to charge to group $j$ its own average claims, being $\overline{X_j}$ as premium charged to the insured. These methods are used if the portfolio is heterogeneous, provided a fairly large claim experience. To compromise these two extreme positions, we take the weighted average of these two extremes:

$C = z_j\overline{X_j} + (1 - z_j) \overline{X}\,$

$z_j$ has the following intuitive meaning: it expresses how "credible" (acceptability) the individual of cell $j$ is. If it is high, then use higher $z_j$ to attach a larger weight to charging the $\overline{X_j}$, and in this case, $z_j$ is called a credibility factor, such a premium charged is called a credibility premium.

If the group were completely homogeneous then it would be reasonable to set $z_j=0$, while if the group were completely heterogeneous then it would be reasonable to set $z_j=1$. Using intermediate values is reasonable to the extent that both individual and group history are useful in inferring future individual behavior.

Actuarial credibility

Actuarial credibility describes an approach used by actuaries to improve statistical estimates. Although the approach can be formulated in either a frequentist or Bayesian statistical setting, the latter is often preferred because of the ease of recognizing more than one source of randomness through both "sampling" and "prior" information. In a typical application, the actuary has an estimate X based on a small set of data, and an estimate M based on a larger but less relevant set of data. The credibility estimate is ZX + (1-Z)M, where Z is a number between 0 and 1 (called the "credibility weight" or "credibility factor") calculated to balance the sampling error of X against the possible lack of relevance (and therefore modeling error) of M.

For example, an actuary has an accident and payroll historical data for a shoe factory that suggest that the accident rate is 3.1 accidents per million dollars of payroll. She has industry statistics (based on all shoe factories) suggesting that the rate is 7.4 accidents per million. With a credibility, Z, of 30%, she would estimate the rate for the factory as 30%(3.1) + 70%(7.4) = 6.1 accidents per million.

References

• Behan, Donald F. (2009) "Statistical Credibility Theory", Southeastern Actuarial Conference, June 18, 2009
• Whitney, A.W. (1918) The Theory of Experience Rating, Proceedings of the Casualty Actuarial Society, 4, 274-292 (This is one of the original casualty actuarial papers dealing with credibility. It uses Bayesian techniques, although the author uses the now archaic "inverse probability" terminology.)
• Longley-Cook, L.H. (1962) An introduction to credibility theory PCAS, 49, 194-221.