Credibility theory

Credibility theory is a form of statistical inference used to forecast an uncertain future event developed by Thomas Bayes. It may be used when you have multiple estimates of a future event, and you would like to combine these estimates in such a way to get a more accurate and relevant estimate. This is typically used by actuaries working for insurance companies when determining the premium values. For example, in group health insurance an insurer is interested in calculating the risk premium, R P {displaystyle RP} , (i.e. the theoretical expected claims amount) for a particular employer in the coming year. The insurer will likely have an estimate of historic overall claims experience, x {displaystyle x} , as well as a more specific estimate for the employer in question, y {displaystyle y} . Assigning a credibility factor, z {displaystyle z} , to the overall claims experience (and the reciprocal to employer experience) allows the insurer to get a more accurate estimate of the risk premium in the following manner: Credibility theory is a form of statistical inference used to forecast an uncertain future event developed by Thomas Bayes. It may be used when you have multiple estimates of a future event, and you would like to combine these estimates in such a way to get a more accurate and relevant estimate. This is typically used by actuaries working for insurance companies when determining the premium values. For example, in group health insurance an insurer is interested in calculating the risk premium, R P {displaystyle RP} , (i.e. the theoretical expected claims amount) for a particular employer in the coming year. The insurer will likely have an estimate of historic overall claims experience, x {displaystyle x} , as well as a more specific estimate for the employer in question, y {displaystyle y} . Assigning a credibility factor, z {displaystyle z} , to the overall claims experience (and the reciprocal to employer experience) allows the insurer to get a more accurate estimate of the risk premium in the following manner: