法务会计外文文献翻译中英文2018Word下载.docx

法务会计外文文献翻译中英文2018Word下载.docx

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中文标题：

大数据与法务会计教育的整合

文献出处：

期刊：

JournalofForensic&

InvestigativeAccounting

第10卷，第1期，2018，87-99页

译文字数：

4000多字

原文

TowardtheIntegrationofBigDataintoForensicAccountingEducationZabihollahRezaee,JimWang

Introduction

Informationtechnology（IT）advances（e.g.,cloudcomputing,electronicsocialmedia,analytics）intherecentdecadesprovideorganizationsofalltypesandsizeswithanunprecedentedamountofdataandinformationavailability.TheseorganizationsareprogressivelymovingintotheageofBigDatathatisdescribedbyGartner（2014）andVasarhelyietal.,（2015）ashigh-volume,high-velocity,andhigh-varietyinformation,thatistypicallyprocessedelectronicallyandintendedtoimprovedecisionmaking.ArecentsurveyofFortune1,000firmsconductedbyVantagePartner（2016）indicatestheincreasinguseofBigData.Approximatelyfifty-fourpercentofthesefirmshaveestablishedrolesofChiefDataOfficerstoincreaseinvestmentinBigData,fastertime-to-answer,fastertime-to-decision,fasterspeed-to-market,andobtaingreaterinsightsintobusinessandcustomers.Forensicaccountingalsohasemergedasanimportantpracticeforaccountingfirms,whichincludevariouspracticessuchasfraudexamination,investigationofcorruptionandbribery,businessvaluation,beinganexpertwitness,cybercrimemanagement/cybersecurity,andlitigationsupport（Crumbleyetal.,2015）.ThisarticleexaminestheintegrationofBigDatainto

forensicaccountingeducationby:

（1）reviewingpriorliteraturetoidentifyalistofBigDatatopicsorcoursesthatcouldbeincorporatedintoforensicaccountingeducation;

（2）investigatingtheforensicaccountingsyllabiofuniversitiesworldwideintheircoverageofBigDatatopics;

and

（3）presentingsuggestionsforthedevelopmentofforensicaccountingcurriculumwithBigDataanddataanalyticsfocus.

Forensicaccountantsarenowfacingthehugeamountofbothstructured（e.g.,generalledgerortransactiondata）andunstructureddata（e.g.,e-mail,voice,orfree-textfieldsinadatabase）,togetherwithanincreasingamountofnontraditionaldatasourcessuchasthird-partywatchlists,newsmedia,free-textpaymentdescriptions,emailcommunications,andsocialmedia.DataanalyticswiththeuseofBigDatahasbeenemployedtotransformunstructureddataintouseful,structured,andrelevantinformationfordecisionmaking.Forensicaccountingservicesareoftenperformedbyindividualswithmultidisciplinaryknowledgeandexperienceinaccounting,technology,andlawswhoareprofessionallyskepticalinaskingrightquestions,utilizingdatascience,anddatamanagementexpertisetotranslatequestionsintomeaningfulanalyticsandusesystemsandITinfrastructures（EY,2014;

EY,2016）.EY（2016）documentstwofactorswhichincreasetheuseofdataanalytictoolsbyforensicaccountantsintheirinvestigations.First,cyberbreachescauseillicittransferringof

funds,disruptcriticaloperations,stealintellectualproperty/confidentialpersonaldata,andothercriticaldigitalassets.Second,fraudrisksincreasebecauseofinsiderthreatstriggeredbymaliciousinsiderstomanipulateordestroydata,perpetratefraud,stealintellectualproperty,engageinunauthorizedtrading,espionage,orinformationtechnologysabotage.

PriorresearchsuggeststhatthesupplyofBigDataprofessionalsisinadequatedespiteanincreasinguseofBigDatainforensicaccountingpractices.Forexample,DongLaney（2012）forecastsashortageofdataspecialistsasitisanticipatedthattheneedfordatascientistsisgrowingfast,andthereisapredictedshortageofabout100,000-plus-personanalytictalentsby2020intheU.S.McKinseyGlobal（2011）predictssignificantshortageof1.5millionprofessionalswithanalyticalexpertiseandtheskillstounderstandanduseBigDataindecision-makingprocesses.Wixometal.,（2014）findgrowingmarketdemandforstudentswithBusinessIntelligence（BI）andBusinessAnalytics（BA）skillsetswiththeanticipationthatdemandforBigDataprofessionalswillriseinthefuture.Recently,PwC（2017,p5）estimates“themarketanalysiscallsforannualjobopeningstorisesteadilyto2.72millionpostingsfordatascienceandanalyticsrolesin2020.”InChina,thereisanestimateddemandof1.8millionBigDataprofessionalsinthenextthreetofiveyears,1.5millionmorethancurrentsupply（GlobalTimes,2017）.

AshortsupplyofBigDataprofessionalsraisesthequestionasto

whetherthereisadequatetrainingrelatedtoBigDataattheundergraduate/graduatelevelintheforensicaccountingeducation.Thisarticleaddressesthisquestionby

（1）investigatingthestatusoftheBigDataintegrationintoforensicaccountingcoursesandprograms;

and

（2）examiningthetopicalcoverageofBigDatainforensicaccountingcourses/programs.OuranalysesshowthatthecoverageofBigDatacoursesinforensicaccountingeducationinChinacurrentlylagsbehindtheUnitedStates（U.S.）,Canada,andotherEnglishspeakingcountries.AnecdotalevidencefromWangetal.,（2016）showsthatthereareonlynineteenuniversitiesinChinawithforensicaccountingcoursesorforensicaccountingprograms,andonlythreeuniversitieshaveastandaloneBigDatacourseoutofthenineteenuniversities.ThissituationcontrastsdramaticallywiththeU.S.,whereamongninety-sevenuniversitiesofferingforensicaccountingprograms,forty-threehaveastandaloneBigDatacourse（SedaandKramer,2014）.Asaresponsetotheincreasingdemand,anaccreditationbodysuchasAssociationtoAdvanceCollegiateSchoolsofBusinessandaccountingfirmsproposedtoincorporateBigDatarelatedskillsandknowledgetopreparestudentsintheircareer（AACSBA7,2014）.PricewaterhouseCoopers（2015）recommendedadata-drivenaccountingcurriculumaspartofitsefforttofacilitatethecurriculumdesignanddeliverywithchallengesofIT,BigData,anddataanalytics.Theresultssuggestmoreresourceinputintothe

forensicaccountingprograms,particularlyatundergraduatelevel,toincorporateBigData/dataanalyticsintothecurriculum.ThisresearchmakesanimportantcontributiontotheforensicaccountingeducationliteraturebyexaminingtheissueofintegrationofBigData/dataanalyticsintoforensicaccountingcoursesandthusthebusinesscurriculum.

LiteratureReview

Severalstudies（e.g.,Chen,Chiang,andStorey,2012;

Cao,Chychyla,andStewart,2015）provideanoverviewofBigDataanditsimplicationsinauditanalytics.Chen,Chiang,andStorey（2012）arguethatBigDataanalyticscanimprovetheeffectivenessoffinancialfrauddetection.Forexample,ameta-learningframework,whichisbasedonabusinessintelligenceanddesignscienceapproach,canachievea79.2percentdetectionrateoftestinstancesinmostlegitimateandfraudcases.Thisdetectionishigherthanthelessthanseventypercentdetectionrateoffinancialfraudswithtraditionalmethods.Cao,Chychyla,andStewart（2015）proposethatBigData/analyticscanimprovetheefficiencyandeffectivenessoffinancialstatementaudits.Auditorsarecurrentlyaccessingtheincreasingamountofvariousinformationsuchasnews,audioandvideostreams,cellphonerecordings,socialmediacomments,becauseofdigitalizationandtheabilitytostorepetabytesofdata.ThisincreaseindataprovidesauditorswithanopportunitytouseBigDataanalyticsinriskassessment,substantiveanalyticalprocedures,collection

ofauditevidence,aswellasobtainingauditconfirmationofevents,transactions,andreportingelements.Auditorscanuseanalyticstoprocessthevoluminousinformationofclients’pastactivitiesoroutcomesofpastauditstoidentifyfraudrisksandtodirectauditefforttowardsfrauddetection.Forexample,auditorsincludingforensicaccountantscanusestatisticalrelationshipsbetweenbusinesselementsandprocessestodetectirregulareventsorfrauds.

BusinessesandregulatorsarealsousingBigDataanddataanalyticsindetectingirregularities,non-compliancewithapplicablelaw,rules,regulations,andstandards.Forexample,internalauditstaffoftwoinsurancefirms,BlueCrossandBlueShield,usedataanalyticstodetectduplicateinsuranceclaimsamongmillionsofclaimseachmonth.（Cao,Chychyla,andStewart,2015）.Withthevoluminoustime-stampeddatafromthirteennationalequityexchanges,whicharearoundonebillionrecordseachday,theSECusesBigDataanddataanalytics,labeled“RobotCop,”toidentifysecuritieslawviolations,andirregularitiesinthepreparationoffinancialstatements,issuerreporting,andauditfailures（Forbes,2013）.BrownLiburd,Issa,andLombardi（2015）discussthechallengesinincorporatingBigDataandauditanalyticsintoauditstrategiesandpresentthebehavioralimplicationsofBigDataforauditjudgmentsthataffectauditquality.TheuseofbothBigDataandDataSciencemethodsarechangingthewayauditorsgatherandassessaudit

evidence.BigDataisexpectedtogrowexponentiallyandthus,forensicaccountantsshouldproactivelysearchforpatterns,includingirregularities,inBigDataandassessandmanagetheirriskprofileindetectingfraud.

IssaandKogan（2014）pointoutthechallengeofuseofBigDataanddataanalyticsbyauditors.Useofdataanalyticscanbeoverwhelmingforauditorsbecauseofthevoluminousoutputofdataresultingfromanalysis.Ontheonehand,irrelevantinformationcanpotentiallylimitthevalueofuseofBigDataanddataanalyticsforauditors.Ontheotherhand,auditorsneedtheknowledgeandskillstoclearlyunderstandthequalityandrelevanceofthedatatomakeprofessionaljudgments.