Originally posted in the RStudio R Community blog, written by Dr. Maria Prokofieva, professor, Victoria University Business School, Australia, and works with CPA Australia. Dr. Prokofieva is a member of the R / Business Working Group which is promoting the use of R in accounting, auditing, and actuarial work. More information on R Consortium Working Groups can be found here.
What is actuarial science?
Actuarial data science lies at the intersection of math and business studies, combining statistical knowledge and methods from insurance and finance areas. Compared to data scientists, actuaries focus more on finance and business knowledge, while still collecting and analyzing data.
The profession is in high demand, and according to the Bureau of Labor Statistics (BLS), it is expected that actuary jobs will a enjoy 24% increase from 2020-30. This is much faster than the average for all occupations. Moreover, the median salary for an actuary is estimated to be over $100,000.
The focus of the field is on assessing the likelihood of future events, particularly in business settings (especially finance and insurance) to plan for outcomes and mitigate risks. With this in mind, probability analysis and statistics are applied to very many areas, such as predicting the number of children for a health insurance or the payout of the life insurance policy. Some common tasks for actuaries include calculating premium rates for mortality and morbidity products, assessing the likelihood of financial loss or return, business risk consulting, pension and retirement planning, and many more. Basically, actuaries perform any tasks that include risk modeling, be that in insurance, financial planning or energy and environment.
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