Actuarial Career for Statistics Graduates
F.K. Louis Ng
The University of Hong Kong, Hong Kong

Actuarial science is a discipline that uses probability and statistics in solving problems related to financial security. Before the proliferation of actuarial science programs in universities towards the end of the last century, most of the actuaries had received university education in mathematics or statistics. Over the last several decades, with greater recognition of the actuarial profession by the public as a rewarding career, many high quality students have chosen actuarial science programs as their university studies. As a result, employers looking for actuarial expertise can choose suitable candidates among graduates from these programs and university graduates majoring only in statistics are usually not considered. However, the factors affecting the actuarial career environment have undergone some significant changes.

In this paper, the following issues will be discussed in the context of how they impact the actuarial career:

(i) Greater emphasis in enterprise risk management;

(ii) Requirement for more vigorous scenario testing and stochastic analysis;

(iii) Shifts in demographic structure creating demand for different financial security products;

(iv) Climatic changes affecting the validity of financial security models based on historical information;

(v) More demanding customers advocating higher levels of consumer rights and

(vi) Technological advances leading companies to search for more effective distribution channels.

These changes have already posed significant challenges for practicing actuaries. However, as the background to these issues and the ensuing problems are usually not covered in standard actuarial science programs, there is a window of opportunity for statistics graduates to convince employers that their expertise in data analysis and statistical modeling can contribute to develop effective and efficient solutions to these new problems.

Keywords: Actuarial career; Data analysis; Statistical modeling