Actuaries usually involve in the development of different kinds of actuarial models. A model is a simple, stylized representation of a real world process or system. Models usually do not capture all of the features or complexities of the real system. Instead, models are designed to confine the essential features of the actual system. Like any other model, actuarial models are also used to understand how the actual system will evolve in the future. It also provides vital information to actuaries that help them make correct decisions while proceeding with the systems.
The actuarial models vary based on the nature of an actuary’s work. Still, most of the actuarial models have interest rates, inflation or future mortality as inputs. For example, life insurance actuaries usually create models that decide the number and size of claims that set up proper reserves. While creating an actuarial model for this purpose, actuaries will have to consider factors that affect the mortality of policy holders including age, gender, medical history etc. In addition to this, possible future expenses for policy administration and the interest that can be earned on reserves are also considered while creating the model. Another case where actuarial models are relevant is in the share market. Suppose, an investor wants to model the maximum price a share will attain in a specific period, so that he can close or sell it at that price. Here, the actuary who creates such a model will have to conduct a thorough market analysis with available data.
Most of the times, actuarial models help anticipate how a particular system will react under different conditions. In other words, actuarial models help actuaries to form an opinion and suggest a course of action for uncertain future events. However, each and every actuarial model could not achieve 100% accuracy. A model becomes satisfactory only if the objectives of the modeling are met satisfactorily. Actuarial models are classified into two types: deterministic models and stochastic models. A deterministic model has a unique output for a given set of inputs whereas a stochastic model has a random variable as output. More pragmatically, the assumptions and equations decide the results in case of a deterministic model. So, the results will be changed only if the assumption or equation is changed. But in case of stochastic model, an element of randomness also plays a role in determining the results. So, you will get different results each time you run the model. In other words, stochastic models provide an array of outputs with a related probability.
There is no “one-size-fits-all” solution when it comes to the development of actuarial models. In other words, there is no exact method or formulaic way for actuarial modeling. Building actuarial models is a time consuming and complex task even for experienced actuaries. While building a model, first of all actuaries set well-defined objectives for the model. Even before starting the development of the actuarial model, it should be planned how to validate the model. Actuaries decide the structure of the model and then collect and analyze parameters of the model. After clearly deciding how to implement the model, the next step is writing and debugging the program. Once the model is tested and validated, the model is ready to use.