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{{More citations needed|date=June 2011}}
'''Predictive analytics''' is a form of [[business analytics]] applying [[machine learning]] to generate a [[predictive model]] for certain [[business]] applications. As such, it encompasses a variety of [[Statistics|statistical]] techniques from [[Predictive modelling|predictive modeling]] and [[machine learning]] that analyze current and historical facts to make [[prediction]]s about future or otherwise unknown events.<ref name=":52">{{Cite web |title=To predict or not to Predict |url=https://mccoy-partners.com/updates/to-predict-or-not-to-predict |access-date=2022-05-05 |website=mccoy-partners.com}}</ref> It represents a major subset of [[machine learning]] applications; in some contexts, it is synonymous with [[machine learning]].<ref name="Siegel 2013">{{Cite book |last=Siegel |first=Eric |title=Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (1st ed.) |publisher=[[Wiley (publisher)|Wiley]] |year=2013 |isbn=978-1-1183-5685-2 |language=English}}</ref>
In business, predictive models exploit [[Pattern detection|patterns]] found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding [[decision-making]] for candidate transactions.<ref>{{Cite book |last=Coker |first=Frank |title=Pulse: Understanding the Vital Signs of Your Business (1st ed.) |___location=Bellevue, WA |publisher=Ambient Light Publishing |year=2014 |isbn=978-0-9893086-0-1 |pages=30, 39, 42, more}}</ref>
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Predictive analytics is a set of [[business intelligence]] (BI) technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behavior and events. Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future.<ref name=":4">{{Cite web |last=Eckerson |first=Wayne, W |date=2007 |title=Predictive Analytics. Extending the Value of Your Data Warehousing Investment |url=http://download.101com.com/pub/tdwi/files/pa_report_q107_f.pdf}}</ref> Predictive analytics statistical techniques include [[data modeling]], [[machine learning]], [[Artificial intelligence|AI]], [[deep learning]] algorithms and [[data mining]]. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.<ref>{{Cite book |last=Finlay |first=Steven |title=Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods (1st ed.) |publisher=[[Palgrave Macmillan]] |year=2014 |isbn=978-1137379276 |___location=Basingstoke |pages=237 |language=English}}</ref> The core of predictive analytics relies on capturing relationships between [[explanatory variable]]s and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.<ref name=":52" />
Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from [[forecasting]]. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions."<ref
== Analytical techniques ==
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=== Analytical Review and Conditional Expectations in Auditing ===
An important aspect of auditing includes analytical review. In analytical review, the reasonableness of reported account balances being investigated is determined. Auditors accomplish this process through predictive modeling to form predictions called conditional expectations of the balances being audited using autoregressive integrated moving average (ARIMA) methods and general regression analysis methods,<ref name=":0" /> specifically through the Statistical Technique for Analytical Review (STAR) methods.<ref name=":3">{{Cite journal |last1=Kinney |first1=William R. |last2=Salamon |first2=Gerald L. |date=1982 |title=Regression Analysis in Auditing: A Comparison of Alternative Investigation Rules |journal=Journal of Accounting Research |volume=20 |issue=2 |pages=350–366 |doi=10.2307/2490745 |jstor=2490745 |issn=0021-8456}}</ref>
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Police agencies are now utilizing proactive strategies for crime prevention. Predictive analytics, which utilizes statistical tools to forecast crime patterns, provides new ways for police agencies to mobilize resources and reduce levels of crime.<ref>{{Cite journal |last1=Towers |first1=Sherry |last2=Chen |first2=Siqiao |last3=Malik |first3=Abish |last4=Ebert |first4=David |date=2018-10-24 |editor-last=Eisenbarth |editor-first=Hedwig |title=Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective |journal=PLOS ONE |language=en |volume=13 |issue=10 |pages=e0205151 |doi=10.1371/journal.pone.0205151 |issn=1932-6203 |pmc=6200217 |pmid=30356321 |bibcode=2018PLoSO..1305151T |doi-access=free }}</ref> With this predictive analytics of crime data, the police can better allocate the limited resources and manpower to prevent more crimes from happening. Directed patrol or problem-solving can be employed to protect crime hot spots, which exhibit crime densities much higher than the average in a city.<ref>{{Cite journal |last1=Fitzpatrick |first1=Dylan J. |last2=Gorr |first2=Wilpen L. |last3=Neill |first3=Daniel B. |date=2019-01-13 |title=Keeping Score: Predictive Analytics in Policing |url=https://www.annualreviews.org/doi/10.1146/annurev-criminol-011518-024534 |journal=Annual Review of Criminology |language=en |volume=2 |issue=1 |pages=473–491 |doi=10.1146/annurev-criminol-011518-024534 |s2cid=169389590 |issn=2572-4568}}</ref>
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Several firms have emerged specializing in predictive analytics in the field of professional sports for both teams and individuals.<ref>{{Cite web |title=Free AI Sports Picks & Predictions for Today's Games |url=https://leans.ai/ |access-date=2023-07-08 |website=LEANS.AI |language=en-US}}</ref> While predicting human behavior creates a wide variance due to many factors that can change after predictions are made, including injuries, officiating, coaches decisions, weather, and more, the use of predictive analytics to project long term trends and performance is useful. Much of the field was started by the Moneyball concept of [[Billy Beane]] near the turn of the century, and now most professional sports teams employ their own analytics departments.
== See also ==
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