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For fellows of the Casualty Actuarial Society, this is fulfilled by taking the nation-specific Exam 6-Canada, instead of Exam 6-United States. [29] Further, the CIA requires three years of actuarial practice within the previous decade, and 18 months of Canadian actuarial practice within the last three years, to become a fellow. [30]
Actuaries also address financial questions, including those involving the level of pension contributions required to produce a certain retirement income and the way in which a company should invest resources to maximize its return on investments in light of potential risk.
The American Academy of Actuaries, also known as the Academy, is the body that represents and unites United States actuaries in all practice areas. Established in 1965, the Academy serves as the profession's voice on public policy and professionalism issues.
The CAS requires all candidates to qualify through a series of actuarial exams covering various aspects of actuarial practice. Passing Exams 1–6 as well as Exam S, the Course on Professionalism, the Validation by Educational Experience (VEE), and two online courses qualifies an actuary for the Associateship designation; passing three additional exams is required to become a Fellow. [10]
Non-members working in the actuarial profession and taking exams are often referred to as actuarial students or candidates. Members of the SOA who meet a professional experience requirement are eligible for membership in the American Academy of Actuaries, which represents United States actuaries from all practice areas. [12]
Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in insurance, pension, finance, investment and other industries and professions. Actuaries are professionals trained in this discipline.
Actuarial science – discipline that applies mathematical and statistical methods to assess risk in the insurance and finance industries. What type of thing is ...
Actuarial credibility describes an approach used by actuaries to improve statistical estimates. Although the approach can be formulated in either a frequentist or Bayesian statistical setting, the latter is often preferred because of the ease of recognizing more than one source of randomness through both "sampling" and "prior" information. In a ...