Exam FAM Details

We start by learning about cost-sharing mechanisms used by insurers and get an introduction to reinsurance.

We study general probability topics, parametric distributions, and risk measures. These concepts are applied to an insurance product as customers file claims of various sizes. We model the claim sizes using a severity model and the number of claims using a frequency model. We also learn to combine the severity and frequency models into a single aggregate model.

After deciding which model to use, the next step is to determine the most appropriate values for the model parameters. We learn about maximum likelihood estimation, which is a common method to estimate parameters.

We learn about classical credibility. We apply it in the context of combining a small specific dataset from a group of customers with a large general dataset from a whole industry.

Loss reserving and ratemaking are two of the most important functions in short-term insurance. We learn a number of techniques for estimating loss reserves and look at how rates are set.

Derivatives are a fundamental part of financial risk management. We look into a type of derivative called options. We learn about the properties of options and how they are used as insurance. We use our first model for pricing options by creating a binomial model for stock prices. The Black-Scholes-Merton option pricing formula is discussed.

We begin to explore long-term actuarial mathematics. We get some background to modern actuarial practice pertaining to long-term, life contingent payments. We also learn key features of insurance coverages and retirement financial security programs.

This section is foundational to subsequent sections. Probability theory forms the foundation for life contingent payment models. We study the laws of probability for mortality in a survival model. We also learn about specific mortality models and assumptions.

We evaluate different types of insurance under a discrete or continuous assumption and examine the relationship between continuous and discrete insurance models. We then evaluate annuity payouts made to a policyholder under a discrete or continuous assumption, the relationship between continuous and discrete annuity models, and the mathematical relationship between insurance and annuities.

We apply concepts from the last two sections to determine premiums charged by insurance companies. We use the equivalence principle, along with other assumptions, to calculate the premium for a particular policy. Expenses are added into the model as an additional payment to the insurer. We learn to calculate and model reserves set aside by an insurance company to remain solvent.

We look at non-parametric ways to estimate quantities such as probabilities and forces of mortality. We also explore the use of the maximum likelihood estimator to create a mortality model. The estimation method depends on which quantity is estimated and the type of data at hand.