會計數(shù)據(jù)分析 Solutions-Manual Chapter-6-EOC-SM

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SolutionsManual–Chapter6

SolutionstoDiscussionQuestions

Naturehelpunderstandauditorsperformtheauditprocedures.Inotherwords,whatarethequestionsthattheauditorsistryingtoanswer?Iftheyweretryingtopredictaclass,theywoulduseaclassificationmodel.Iftheyweretryingtopredictavalue,regressionwouldbeappropriate.

Extentindicateshowmuchtheauditorwilltest.Astheyapplydataanalytics,theymaywanttodecidetotestasampleoftransactionsortheentirepopulation.Theaccessibilityofthedatawilldrivethistoacertainextent.

Timingtelltheauditorhowoftentoperformtheaudit.Theyshouldtrytomatchthepulseofthebusiness.Forexample,theymaywanttotestsalestransactionsdaily,depreciationcalculationsmonthly,andestimatesannually.

Answerswillvary.Dataanalyticsaddsvaluewhenthescopeoftheprocedureisclearlydefinedandexpectationsarereasonable.Withclearobjectiveandoutcomesforthedataanalytics,auditdepartmentscanaddvaluetotheauditprocess.

Predictiveanalytics,suchasclassificationorregression,identifypatternsandusethatmodeltopredictanewunknownobservation.Forexample,isanewtransactionlikelytobefraudulent?Prescriptiveanalyticsapplymodelstorecommendfutureactionsbasedonpastresults.Forexample,decisionsupportsystemsandartificialintelligencetakeinputandrecommendanactionforsimilarevents.

Bothpredictiveandprescriptiveanalyticsmakefuturepredictionsbasedonpastdata,buttheformergenerateadditionaldatawithoutguidance,whilethelatterprovidesthatadditionalguidance.Bothmayresultinaction,butprescriptivewouldrecommendthespecificactiontotake.

Inatraditionalorcontinuousaudit,predictiveanalyticsmayalertauditorstocustomerswithbaddebtsorpossibleexploitationofanemployeeincentiveprogramandprompttheauditorstoinvestigate.Prescriptiveanalyticswouldsuggestacourseofaction.Forexample,giventheinputanAImodelwouldsuggestthatotherauditorsinasimilarsituationtookaspecificactionandwouldrecommendthesame.

Auditorscouldoffloadroutineactionstotheanalyticalproceduresotheycanusetheireffortandexpertisetounderstandtheunderlyingissue.However,auditorswouldalsobeabletousethesesystemstoconfirmacourseofactiontheywereintendingtotakeinthefirstplace.Thedangerisoverrelianceonthesesystems,especiallyiftheunderlyingdatainincompleteorinvalid.

Sortingdataandcalculatingsummarystatisticsareusefulprocedurespartlybecausetheyprovidevaluableinsightwithlittleeffort.Summarystatisticsareusefulforvalidatingdata(e.g.makingsureitiscomplete)aswellasprovidingavaluetocompareagainstabenchmark(e.g.thestatsfromthepreviousperiod).Insomecases,extremevaluesmayindicateover-andunder-performanceofemployees,divisions,etc.Inothercases,theymayindicateriskthatwillaltertheauditscopeandhelpauditorsfocusonitemswithgreaterexposure,forexample,orthesehighvaluescouldpotentiallybefraud.

SolutionstoProblems

Managersusetheagingofaccountsreceivabletoestimatethebaddebtexpenseontheincomestatement,asaccountswitholderopenbalancesarelesslikelytobecollected.Aswithmostestimates,auditorsshouldfocusonthevaluemanagementassignsandtestitforappropriateness.Agingofaccountsreceivableisasimpletooltocomparethisestimatewithpastcollections.

Inacontinuousaudit,adashboardwithagedaccountsreceivableswouldprovideanindicatorofthechangesinthebuckets(e.g.0-30days,31-60days,etc.).Whenauditorsdefineanacceptablelimitineachcategory,theywouldreceiveanalertwhenaparticularbucketexceededthatrangeandinvestigatewhethertherewasanewissuewithcollectabilityorotherdrivingcausethatmayrequiremanagementtorevisetheirestimate.

Fullpopulationtestingisusefulwhenthesystemcontainsalloftherelevantdata.Auditorsusepopulationtestingtofocustheirauditonriskytransactionsorrecords(e.g.outliers)ratherthanevaluatinglowriskroutinetransactions.

Samplingisstillusefulwhenauditorsneedtotestthedetailsofthetransactionsandunderlyingdocumentation(e.g.asignatureonaninvoice)thatisnotreadilyinthesystemforexistenceandcompleteness.Assumingthesampleoftransactionsarecomplete,theycaninferthattheremainingtransactionsarestatisticallycompleteaswell.

Instatistics.99.7%oftransactionsfallwithin+/-3standarddeviationsfromthemean.Anythingabove+3orbelow-3hasaveryrareprobabilityofhappeningbychance.Whenanauditorassignsaz-scoretotransactionsandsortthevalues.Theseoutliersrepresentrarevaluesandrisk.Diagnosticanalysisenablesauditorstoassignthesescoresandidentifyoutliers.

Gapsandmissingvalues,duplicatetransactionsordata,outliers,atypicalactivity,groupsofdata,plausiblerelationships.Whilemostoftheseanalysesfocusonnumericalvalues,suchastransactionidentifiersanddollarvalues,ananalysisoftextfields,dates,andotherattributesmayhelpidentifyotheranomalousrelationships.

Stratificationisusefulforsortingtherecordsbydatepatternsorspecificemployeestoidentifythesepatterns.Z-scoremayfindthoseemployeeswithstatisticallyraretotals.Drill-downcouldbeusefulinevaluatingtheindividualtransaction.Auditorsdeterminewhethertheemployeeisfollowingtheappropriateprocessandthattheyarenotpurposefullycircumventinginternalcontrols.Exceptionsrepresentrisk,butnonecessarilyaproblem.

Benford’slawappliestolargesetsofnumbers,sodollaramountsoftransactionsarethemostlikelytarget.Atestofsalestransactionsmayshowsequencesoftransactionsthatareatypicalthathavebeenperformedbyspecificemployeesorcustomers,suchaschannelstuffing.Atestofautomaticallyassignedvendornumberandemployeenumberswouldnotshowanythingofvalue.Invoicenumbersandchecknumberslikewisewouldrevealverylittle.Benford’sLaw’svaluediminishessignificantlyanytimeyouhavesystematically-assignednumbersorsmallsamplesizes

Artificialintelligencemaybeusefulinhelpingtheauditorknowwhethertopushbackon