Some comments and suggestions from a Mathematics student who first graduated and then (successfully) pursued an Actuarial career
I think the other actuaries who have contributed here have given great advice for how to become an actuary. There is also a lot of good advice that can be found elsewhere on the internet. Instead, I would like to focus more on what actuaries do, to help students decide if they want to become actuaries. I will mostly stick to my experiences, which of course are not representative of all possible experiences. In fact, I am a U.S. citizen who returned to the U.S. after graduating from UBC. I wanted to work in the health insurance industry, and that is where I ended up. Nevertheless, I think my experiences are very relevant to UBC students who are considering entering the actuarial profession.
What do actuaries do.
Generally speaking, actuaries help companies deal with uncertainty. They are most commonly employed in the insurance and pension industries, because these types of products pay out an uncertain amount of money. For example, an auto insurance policy may have to pay $5,000 for an accident, or it may have to pay $30,000 for an accident, or there may be no accident at all. As another example, a pension may pay $50,000 per year for the remainder of a person's life, but the future lifetime of that person is uncertain. He may live for five years or thirty-five years. Companies that offer products like these often employ actuaries to make sure their products are sustainable and profitable. That leads to the two traditional roles an actuary can play.
The first traditional actuarial role is called 'pricing', and the second is called 'reserving'. Pricing actuaries determine the price to charge for a product. They want the price to cover the uncertain costs of the product, but also to be competitive. Reserving actuaries determine how much money the company should hold in reserve, to pay claims for future events and for events that have already occurred but have not yet been reported. I work in pricing, so I will mostly talk about that.
In pricing, we build mathematical models to try to predict how much we will have to pay in claims, and how much we will get in revenue. Since we set the prices, we are pretty good at predicting our revenue. Our models are usually built in Excel, and can become quite complex, consisting of several workbooks. For example, one workbook might model the effect of a demographic shift in our population. Another workbook might model how the number of weekdays in a month affects our cost for that month. The outputs of these models will then be fed into a more comprehensive model that will use them to estimate our future cost, which itself is an input to our pricing model. Often models are fed to each other by VBA code, which is useful for automating repetitive tasks in Microsoft Office.
Data Driven Predictions
Everything actuaries do is data driven. For example, the average claims cost for a certain age bracket could be computed based on past data. Then when we see someone in that age bracket in our current data, we could partially predict their cost based on their age. Many factors like this are combined to predict future costs. projects.
Most of what I do as a relatively new actuary is refine our predictive models. I want to better understand how changes in our current data will affect our future costs. To do this I carry out studies based on our data. The first step, after coming up with an idea, is to pull data from servers that house it. I write SAS code to pull and manipulate the data. Some companies use SQL instead of SAS, I think because it is cheaper. There always seem to be data errors that need to be filtered out. I spend a lot of time understanding the data and figuring out how to clean them, so that I can do a reliable study. After I have clean data, summarized at the level I want, I export them to Excel, which is usually where I model. There I look for patterns that I think will help me predict future costs. Having the guidance of a more experienced actuary is very important here, as I don't always know what patterns to look for. If a strong pattern is found, I can use it to better model our future costs.
I think that is a good summary of what I do. Lastly, I would like to give one piece of advice for any student who would like to try a career as an actuary. If you still have time, pass an actuarial exam or two and get an internship while you are in school. That is the easiest, but not only, way to get into the profession. I decided to become an actuary pretty much the day I graduated, and it took me a year to get an internship, which I was then able to turn into a full time job.