Abstract
Predictive analytics makes predictions about unknown future events by exploiting patterns within big data. Predictive models examine patterns found in historical and transactional data to identify hazards and opportunities. Models aim to determine the likelihood of certain results. Predictive analytics involves a range of statistical techniques from data mining, predictive modeling, and machine learning. These statistical techniques isolate trends that businesses want to act on immediately, as well as predictions that identify risks and opportunities which businesses want to act on in the future. In the insurance industry, predictive analytics is typically used in sales, marketing, or underwriting. Used in this way, it ensures companies do not underprice their products by insuring high risks at lower premiums, and it reduces fraud by using algorithms to discover unusual patterns in claims data that are typical of fraud.
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