Interview with Nikita Prabhu - General Insurance Actuary

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Ques 1: Why did you choose Actuarial Science as a career? Ans: I came to know about Actuarial Science when I was in high school from my father who is an insurance agent. He showed me the Ready reckoner for premium rates and told me that ‘actuaries’ were behind the mathematics of it. I researched the profession and found it quite fascinating. I could apply the knowledge gained from the study of mathematics, statistics, economics, and finance to solve a range of real-world problems. It seemed highly rewarding. Ques 2: How is it like to work in both consulting and core Insurance based company environments? Ans: I was fortunate to start my career in consulting with Ernst & Young. Early on in my career, I got exposure to the different fields that actuaries work in, such as life insurance, employee benefits and general insurance. This initial experience aroused my curiosity towards general insurance (GI) and hence I chose to become a GI actuary. In a consulting firm, you get the

Bayesian Statistics: In a layman terms

 What is Bayesian Statistics: (I will try to explain in easy terms)

 

Often researchers investigating an unknown population parameter have information available from other sources in advance of the study that provides a strong indication of what values the parameter is likely to take. This additional information might be in a form that cannot be incorporated directly in the current study. The classical statistical approach offers no scope for the researchers to take this additional information into account. However, the Bayesian statistics is the approach which allows to take this additional information into account while estimating a population parameter.

 

Let me explain you with the help of an example:

 

4 championship races had been done between Mr. A and Mr. B. 

Out of which A has won 3 races and B has won 1 race. So, on whom are you going to bet your money in the next race?






You will Say Mr. A because P(A) = 0.75 and P(B) = 0.25

So your initial estimate about B is P(B) = 0.25

Now I will give you additional information say, there was a rain when Mr. B won and there was rain once when Mr. A won. And in the next match there will definitely be a rain.


So now I ask you again on whom will you bet your money?

Let’s decode the answer:

1.  P(R) = 0.50 (Because rain happened twice out of 4 matches)

2. P(R|B) = 1 (Because whenever Mr. B won there was a rain)

So I want to find out that what is probability that in the next race Mr. B will won if it is given that there will be a rain:

P(B|R) = P(R|B)*P(B)/P(R) = 0.50


I hope you know how this formula comes up otherwise you can mention me in comments I will tell you how.


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Conclusion: Initially we comes up with an answer that P(B) = 0.25 which is my prior estimate and then I give additional information about rain which we incorporated in the form of conditional probability i.e. P(R|B) = 1 and then ultimately we find P(B|R) which is my posterior probability.


So you see how with the help of Bayesian statistics I incorporated additional information into my current study and how my value changes from 0.25 to 0.50.

 

Statistics seems easy now. 😊

Its an art and you are an artist.


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