Actuarial modeling uses equations to predict insurance company costs and probabilities of events. Actuaries use scientific approaches to create models, with two types: deterministic and stochastic. Good information is crucial to the success of the model.
Actuarial modeling is the name of a set of techniques used in the insurance industry. These models are composed of equations that represent the operation of insurance companies, which represent the probabilities of the events covered by the policies and the costs that each event presents to the company. They help companies decide which policies to adopt and set premiums based on the projected claims they will have to pay. They are important because insurance companies use them to keep companies solvent; The models predict the funds that companies will have to pay out, so they know how much money they have to take in to cover their costs.
Insurance companies are organizations that allow policyholders to share risks with one another. The company accepts payments, called premiums, in exchange for a guarantee that it will give money to the policyholder for some specific event. In effect, all policyholders are dividing the cost of events that occur each period so that no one has to pay the entire expense.
An actuary is a person who works for an insurance company and makes sure that it collects enough premiums to cover overhead and claims that policyholders file. Actuaries use scientific approaches that combine probability theory, economic theory, and other disciplines. They use behavioral assumptions derived from these theories to create systems of equations that represent events that happen in the real world. This practice is called actuarial modelling.
The two basic types of models used in actuarial modeling are deterministic models and stochastic models. Deterministic models are the simpler of the two, and were the first to be used. They use probability estimates for each event, and predict the number of events that will actually happen based on these estimates. Stochastic models allow for more randomness, but require more computational power. A computer simulates events over a period hundreds or thousands of times, and based on the results of its simulations, it predicts how many events will happen.
The type of model used is of little importance if the actuary does not have good information about the events he is predicting. In actuarial modelling, the probability of each event and the equations that describe the behavior of people are crucial to the success of the model. Actuaries constantly revise the models so that they generate better predictions.
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