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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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=== Policing ===
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>
=== '''Sports''' ===
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 department.
== See also ==
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