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The insurance industry is essential to the world economy and many companies offer coverage for a wide range of risks that people and businesses face everyday. From a consumer's perspective, the transaction may be as simple as paying a premium every month in exchange for the piece of mind of knowing that recovery from a loss is possible. Within a complex framework of government regulation, an insurance company must evaluate risks and assign probabilities to determine a price for the protection sought. They need to invest funds and even seek to insure their own risks with other insurers. The marketplace demands efficiency and insurance companies are turning to technology to address the need. This article discusses the basic components on an insurer's business model and the fundamental forces that affect the management of an insurance enterprise.

Actuarial models are the tools that actuaries use to represent or simulate future contingent events. A contingent event is one in which its timing and severity is unknown. Contingent or risky events cannot be avoided with 100% certainty; the best that can be done to mitigate the effects of risk is to insure against its financial impact. Actuaries conduct analyses using historical data to predict the likelihood that a future event will occur. Variables, which are characteristics of a certain entity set, are used as inputs into actuarial models to approximate the behavior of reality. Common variables used in actuarial models include: Age, gender, marital status and zip code. Common variables help to predict with some accuracy the outcome of some actuarial models. Today, actuarial models are using more complex variables and a larger number of variables to model future outcomes. Data mining of large databases allows actuaries to build complex predictive models. Predictive models look for pattern matching or correlations between data points; these correlations allow actuaries to model complex realities that were never before possible. Some of the applications that are emerging as a result of predictive analytics are offering strategic advantage to companies who use these powerful models to plan for future risk events and their financial impact. Value-based actuarial models and predictive analytics for identifying insurance claims fraud are two examples of how actuarial models are evolving into powerful tools that offer a competitive advantage in the marketplace.

Actuarial science, and specifically probability and statistics, deal with the concepts of uncertainty and risk. Many of the decisions that we make everyday involve uncertainty; actuarial statistics are used by actuaries to assess the financial risk that is inherent in insurance, pensions, or investment plans. There are numerous Research Starter articles related to this topic. The list below includes the most relevant Research Starter items.

In Mathematics, applied the 1920s, the German military adapted, from a then recently developed business device, and began using an encryption code device known as the “Enigma” machine. Believing it to be an unbreakable encryption device, they continued to employ it into World War II. A significant effort to crack the Enigma code was undertaken, first by Polish mathematicians prior to the German invasion of Poland and then by British mathematicians at Bletchley Park, culminating in the breaking of the code in 1940. This success was instrumental in the ultimate Allied victory and in shortening the war significantly.

DEFINITION: Formation of judgments and predictions regarding dangerous behavior through the application of formulas to particular variables and statistics in preparation for the adoption of necessary preventive measures.