Actuarial science in the context of "Actuary"

Verisk Analytics, Inc. is an American multinational data analytics and risk assessment firm based in Jersey City, New Jersey, with customers in insurance, natural resources, financial services, government, and risk management sectors. The company uses proprietary data sets and industry expertise to provide predictive analytics and decision support consultations in areas including fraud prevention, actuarial science, insurance coverage, fire protection, catastrophe and weather risk, and data management.

The company was privately held until an initial public offering on October 6, 2009, which raised $1.9 billion for several of the large insurance companies that were its primary shareholders, making it the largest IPO in the United States for the year. The firm did not raise any funds for itself in the IPO, which was designed to provide an opportunity for the firm's casualty and property insurer owners to sell some or all of their holdings and to provide a market price for those retaining their shares. The 2009 IPO was priced at $22 per share for 85.25 million shares owned by its shareholders, including American International Group, The Hartford and Travelers, making it the largest since the 2008 IPO for Visa Inc. In an action described by investment research company Morningstar as a "vote of confidence" in Verisk, Berkshire Hathaway was the only company among the firm's largest shareholders that did not sell any of its stock in the October 2009 IPO.

International development or global development is a broad concept denoting the idea that societies and countries have differing levels of economic or human development on an international scale. It is the basis for international classifications such as developed country, developing country and least developed country, and for a field of practice and research that in various ways engages with international development processes. There are, however, many schools of thought and conventions regarding which are the exact features constituting the "development" of a country.

Historically, development was largely synonymous with economic development, and especially its convenient but flawed quantification (see parable of the broken window) through readily gathered (for developed countries) or estimated monetary proxies (estimated for severely undeveloped or isolationist countries) such as gross domestic product (GDP), often viewed alongside actuarial measures such as life expectancy. More recently, writers and practitioners have begun to discuss development in the more holistic and multi-disciplinary sense of human development. Other related concepts are, for instance, competitiveness, quality of life or subjective well-being.

The Mathematical Sciences are a group of areas of study that includes, in addition to mathematics, those academic disciplines that are primarily mathematical in nature but may not be universally considered subfields of mathematics proper.

Statistics, for example, is mathematical in its methods but grew out of bureaucratic and scientific observations, which merged with inverse probability and then grew through applications in some areas of physics, biometrics, and the social sciences to become its own separate, though closely allied, field. Theoretical astronomy, theoretical physics, theoretical and applied mechanics, continuum mechanics, mathematical chemistry, actuarial science, computer science, computational science, data science, operations research, quantitative biology, control theory, econometrics, geophysics and mathematical geosciences are likewise other fields often considered part of the mathematical sciences.

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its opposite (in specific domains, variously called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy.

In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data. The concept, as old as Laplace, was reintroduced in statistics by Abraham Wald in the middle of the 20th century. In the context of economics, for example, this is usually economic cost or regret. In classification, it is the penalty for an incorrect classification of an example. In actuarial science, it is used in an insurance context to model benefits paid over premiums, particularly since the works of Harald Cramér in the 1920s. In optimal control, the loss is the penalty for failing to achieve a desired value. In financial risk management, the function is mapped to a monetary loss.

In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

A time series is very frequently plotted via a run chart (which is a temporal line chart). Time series are used in statistics, actuarial science, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.

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. They can also be explained as a long-term mathematical way to measure a population's longevity. Tables have been created by demographers including John Graunt, Reed and Merrell, Keyfitz, and Greville.

There are two types of life tables used in actuarial science. The period life table represents mortality rates during a specific time period for a certain population. A cohort life table, often referred to as a generation life table, is used to represent the overall mortality rates of a certain population's entire lifetime. They must have had to be born during the same specific time interval. A cohort life table is more frequently used because it is able to make a prediction of any expected changes in the mortality rates of a population in the future. This type of table also analyzes patterns in mortality rates that can be observed over time. Both of these types of life tables are created based on an actual population from the present, as well as an educated prediction of the experience of a population in the near future. In order to find the true life expectancy average, 100 years would need to pass and by then finding that data would be of no use as healthcare is continually advancing.

In probability theory, statistics and related fields, a Poisson point process (also known as: Poisson random measure, Poisson random point field and Poisson point field) is a type of mathematical object that consists of points randomly located on a mathematical space with the essential feature that the points occur independently of one another. The process's name derives from the fact that the number of points in any given finite region follows a Poisson distribution. The process and the distribution are named after French mathematician Siméon Denis Poisson. The process itself was discovered independently and repeatedly in several settings, including experiments on radioactive decay, telephone call arrivals and actuarial science.

This point process is used as a mathematical model for seemingly random processes in numerous disciplines including astronomy, biology, ecology, geology, seismology, physics, economics, image processing, and telecommunications.