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In many countries, actuaries must demonstrate their competence by passing a series of rigorous professional examinations focused in fields such as probability and predictive analysis. Actuarial science includes a number of interrelated subjects, including mathematics, probability theory, statistics, finance, economics, financial accounting and ...
An actuary is a professional with advanced mathematical skills who deals with the measurement and management of risk and uncertainty. [1] These risks can affect both sides of the balance sheet and require asset management, liability management, and valuation skills. [2]
Actuarial science – discipline that applies mathematical and statistical methods to assess risk in the insurance and finance industries. What type of thing is ...
The Society of Actuaries' requirements for Associateship (ASA) include passing 6 preliminary examinations (probability, financial mathematics, fundamentals of actuarial mathematics, statistics for risk modeling, predictive analytics, and one from either advanced long-term actuarial mathematics or advanced short-term actuarial mathematics ...
The actuarial control cycle is a specific business activity which involves the application of actuarial science to real world business problems. The actuarial control cycle requires a professional within that field (i.e., an actuary ) to specify a problem, develop a solution, monitor the consequences thereof, and repeat the process. [ 1 ]
In actuarial science and demography, a life table (also called a mortality table or actuarial table) is a table which shows, for each age, the probability that a person of that age will die before their next birthday ("probability of death"). In other words, it represents the survivorship of people from a certain population. [1]
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.
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