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IX. Predictive AnalyticsPredictive Analytics' encompasses a variety of statistical techniques from datamining, predictive modelling, and machine learning, that analyze current and historicalfacts to make predictions about future or otherwise unknown events.In business, predictive models exploit patterns found in historical and transactional datato identify risks and opportunities. Models capture relationships among many factors toallow assessment of risk or potential associated with a particular set of conditions,guiding decision-makingfor candidate transactions.The defining functional effect of these technical approaches is that predictive analyticsprovides a predictive score (probability) for each individual (customer, employee,healthcare patient, product SKU, vehicle, component, machine, or other organizationalunit) in order to determine, inform, or influence organizational processes that pertainacross large numbers of individuals, such as in marketing, credit risk assessment, frauddetection, manufacturing, healthcare, and government operations including lawenforcement.Predictive analytics is used in actuarial science, marketing, financialservices, insurance, telecommunications, retail, travel, mobility, healthcare, childprotection, pharmaceuticals, capacity planning, social networking and other fields.One of the best-known applications is credit scoring, which is used throughout financialservices. Scoring models process a customer's credit history, loan application, customerdata, etc., in order to rank-order individuals by their likelihood of making future creditpayments on time.DefinitionPredictive analytics is an area of statistics that deals with extracting information fromdata and using it to predict trends and behavior patterns. The enhancement ofpredictive web analytics calculates statistical probabilities of future events online.Predictive analytics statistical techniques include data modeling, machine learning, AI,deep learning algorithms and data mining. Often the unknown event of interest is in thefuture, but predictive analytics can be applied to any type of unknown whether it be inthe past, present or future. For example, identifying suspects after a crime has beencommitted, or credit card fraud as it occurs. The core of predictive analytics relies oncapturing relationships between explanatory variables and the predicted variables frompast occurrences, and exploiting them to predict the unknown outcome. It is important tonote, however, that the accuracy and usability of results will depend greatly on the levelof data analysis and the quality of assumptions.Predictive analytics is often defined as predicting at a more detailed level of granularity,i.e., generating predictive scores (probabilities) for each individual organizationalPage 121 of 211