{"id":5236,"date":"2016-05-07T11:12:33","date_gmt":"2016-05-07T16:12:33","guid":{"rendered":"http:\/\/www.ssc.wisc.edu\/~jfrees\/?page_id=5236"},"modified":"2016-12-29T10:06:54","modified_gmt":"2016-12-29T16:06:54","slug":"insurance-processes","status":"publish","type":"page","link":"https:\/\/users.ssc.wisc.edu\/~ewfrees\/loss-data-analytics\/introduction-to-loss-data-analytics\/relevance-of-analytics\/insurance-processes\/","title":{"rendered":"Insurance Processes"},"content":{"rendered":"

One way to describe the data arising from operations of a company that sells insurance products is to adopt a granular approach. In this “micro” oriented view, we can think specifically about what happens to a contract at various stages of its existence. Consider Figure 1.1 that traces a timeline of a typical insurance contract. Throughout the existence of the contract, the company regularly processes events such as premium collection and valuation, described in Section 1.3; these are marked with an “x” on the timeline. Further, non-regular and unanticipated events also occur. To illustrate, times “t2<\/sub>” and “t4<\/sub>” mark the event of an insurance claim (some contracts, such as life insurance, can have only a single claim). Times “t3<\/sub>” and “t5<\/sub>” mark the events when a policyholder wishes to alter certain contract features, such as the choice of a deductible or the amount of coverage. Moreover, from a company perspective, one can even think about the contract initiation (arrival, time t1<\/sub> and contract termination (departure, time t6<\/sub> as uncertain events. <\/p>\n

\"StochOperationsD\"<\/a>

\n Fig. 1.1. Timeline of a Typical Insurance Policy. Arrows mark the occurrences of random events. Each “x” marks the time of scheduled events that are typically non-random.<\/figcaption><\/figure>\n
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Does This Make Sense?<\/h2>\n

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