MYTHS SURROUNDING ACTUARIAL ANALYTICS

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Prashant K. Rai is currently working as Assistant Manager- Actuarial Services and has experience of 2.5 years (16 months in Analytics and 14 months in Actuarial Analytics). During our conversation, he shared his thoughts about the relationship between Data Science and Actuarial Analytics. Below is an edited excerpt of the same.

Q. What is Analytics? How is it related with Actuarial Science?

Analytics is basically application of mathematics and statistics to come out with a solution in business problem. It is more of business analytics, web analytics, risk management, forecasting and predictive modeling. Data Science (Analytics) and Actuarial Science are perfectly correlated. If you are good in statistics you may pursue both Data Science as well as Actuarial Science. There is huge demand of actuarial students in data science but supply is less, on the other hand demand of actuarial students in core actuarial work is less and supply is more.All the tools of Actuarial Science such as GLM modelling, time series modelling are practiced in Data science as well. So actuarial students should also consider working in data science and if given an opportunity, they can move to actuarial science.

Q. What would you say are the ‘hot’ topics in your industry?

Predictive modeling: Suppose you are about to establish a relationship in 2 independent entities. For example, if you want to predict how much demand for certain things is about to happen today given certain parameters which directly or indirectly influence demand. So first you need to find the mathematical equation between the certain parameters and demand. Then go for regression or establish a Generalized Linear Model between the two entities. For this, first of all see the kind of distribution it follows and simply establish the GLM where distribution belongs to certain exponential family and run the GLM. Through that find the expected value of demand and this expected value now becomes your benchmark after studying all the factors which influence demand. Prediction is all about
finding the expected value.
Risk Management: This apparently seems a larger word but the basic point is it’s about analyzing the data for identification & quantification of those risks which have negative impact on our business. For example, if I am
producing an insurance product within the market, I have certain assumptions about the performance of that product. If my assumption is not meeting the practicality, I have to find out what has been wrong. While you are calculating
these things, you will come to know that there are certain more things which should have been considered while architecting that product. Because we fail to identify those factors, the performance of the product and my expectations are not meeting. So finding out those factors is identification of risk. Now actuaries are appointed in an organization to quantify those risks through statistical or actuarial methodology.This is a brief idea of risk management.

Q. What is the scope of growth and promotion in Actuarial Analytics?

As far as promotion is concerned, it is not directly proportional to number of papers cleared. Though you get an increment on each paper you clear but your promotion and growth depends on your experience, expertise and the grasp you hold on the concepts. Yes, the ultimate goal should be to clear 15 papers and become an Actuary but one should not forget that the experience and knowledge you gain through your work and colleagues cannot be substituted.

Q. How is studying Actuarial Science different from practicing Actuarial Science?

As a student, we look at a mathematical equation and then relate it to the real life situations. But an Actuarial/ Analytics professional does exactly the opposite. We get a business problem and then we have to write the mathematical equation to generate the solution. But you can do so only if you have understood and not mugged up the mathematical equations.

Q. What advice do you give to student actuaries who are about to start their career in Actuarial Science?

To every student actuary, my advice will be to learn the data. Data is everything, Data is god. Yes you can gain a lot of knowledge from the book but if you cannot comprehend the data you cannot employ that knowledge. Be very friendly with the numbers. Try to grab some opportunity where you can work with data and numbers. Moreover, you should be articulate. You should be very good in solving puzzles because most of the companies check your logical ability by giving you puzzles. Also, I will add pragmatic approach, good communication skills and being friendly with the computer.

Q. How much money will I make as an actuary across different levels?

Well, I would not like to put any number as it would be vague. But let me tell you that you will be better off than most of the people around you. You will never regret doing MBA from very reputed colleges if you practice data science or actuarial science. The beautiful thing is – In the longer run, you will direct those people rather than they direct you. This is the biggest statement I can make beyond my shoes.

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