審計(jì)與技術(shù)報(bào)告 2019年 -Audit and technology

ThinkAhead

CHARTEREDACCOUNTANTSTMAUSTRALIA+NEWZEALAND

Auditandtechnology

June2019

AboutACCA

ACCA(theAssociationofCharteredCertifiedAccountants)istheglobal

bodyforprofessionalaccountants,offeringbusiness-relevant,first-choice

qualificationstopeopleofapplication,abilityandambitionaroundtheworldwhoseekarewardingcareerinaccountancy,financeandmanagement.

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countries,helpingthemtodevelopsuccessfulcareersinaccountingandbusiness,withtheskillsrequiredbyemployers.ACCAworksthroughanetworkof110officesandcentresand7,571ApprovedEmployersworldwide,and328approvedlearningproviderswhoprovidehighstandardsoflearninganddevelopment.

Throughitspublicinterestremit,ACCApromotesappropriateregulationofaccountingand

conductsrelevantresearchtoensureaccountancycontinuestogrowinreputationandinfluence.

ACCAhasintroducedmajorinnovationstoitsflagshipqualificationtoensureitsmembersandfuturememberscontinuetobethemostvalued,uptodateandsought-afteraccountancy

professionalsglobally.

Foundedin1904,ACCAhasconsistentlyhelduniquecorevalues:opportunity,diversity,innovation,integrityandaccountability.

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AboutCharteredAccountantsAustraliaandNewZealand

CharteredAccountantsAustraliaandNewZealand(CAANZ)isaprofessional

bodycomprisedofover120,000diverse,talentedandfinanciallyastutememberswhoutilisetheirskillseverydaytomakeadifferencefor

businessestheworldover.

Membersareknownfortheirprofessionalintegrity,principledjudgment,financialdiscipline

andaforward-lookingapproachtobusinesswhichcontributestotheprosperityofournations.Wefocusontheeducationandlifelonglearningofourmembers,andengageinadvocacyandthoughtleadershipinareasofpublicinterestthatimpacttheeconomyanddomesticand

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business.Together,ACCAandCAANZrepresentthevoiceofmembersandstudents,sharingacommitmenttoupholdthehighestethical,professionalandtechnicalstandards.

?TheAssociationofCharteredCertifiedAccountants

Auditandtechnology

Aboutthisreport

Thisreportprovidesanoverviewofsomeofthevarioustechnologiesthatcurrentlyaffectorarelikelytoaffecttheauditprofessioninthenearfutureandwhatthismeansforauditorsaspeople.Thereportissupportedbyexistingresearch,paneldiscussionsheldinGreece,CzechRepublicandSlovakiaandbyinterviewsofleadingpractitioners.

ACKNOWLEDGEMENTS

ACCAandCAANZwouldliketothankMickJames,theAuditandTechnologyeventpanellistsheldinGreece,CzechRepublicandSlovakiaandtheintervieweesfortheirvaluableinputonthisreport.

4

Foreword

MaggieMcGhee

ExecutiveDirectorGovernance,ACCA

SimonGrant

GroupExecutive,AdvocacyandProfessionalStanding,CAANZ

Technologyistransformingtheaccountancyprofession,andhasthepotentialtorevolutioniseaudit.

Roboticprocessautomation,dataanalytics,artificialintelligence,

machinelearning,distributedledgertechnology…tonamebuta

few:aseeminglyendlesslistoftransformationaltechnologiesat

varyingstagesofevolutionisalreadyhaving,andwillcontinueto

have,anindelibleimpactontheauditprocess.Technologyofcourseisneverthepanaceatoresolvingallthecurrentchallengesinaudits,orconverselyseizingallofitsfutureopportunities.

Likealltransformationalstories,technologyintheauditstoryistheenabler;anenablertorenewprocessesthatimprovequalityand

increaseefficiency.Itisalsoacatalystthatwillhelpshiftthefocusoftheauditprocessfromaretrospectiveviewtoonewhichis

prospective,enablingmuchdeeperinsightstoclientsandan

enrichednarrativeoncorporateperformanceanditssustainability

forthefuture.Yetitisthenexusofemergingtechnologywithhumanendeavour,skillandjudgementwhererealfuturevaluefromauditingwillbeunlocked.Inthefaceofexplodingtechnologies,audit

remainsatitsheartaveryhumanactivity.Thatsaid,digital

developmentscouldhaveprofoundimplicationsforhowauditors

conducttheiractivities,aswellaspotentiallyraisingnewethicalandmoralconsiderations.

Thisreportassessesthetechnologieshavingmostimpactonthe

auditprofessionasweknowittoday.Drawingonexistingresearch

andexploringtheviewsofleadingpractitioners,itprovidesan

understandingofhowthechangingbusinessenvironmentisshapingtechnologicalchangeinauditing.Italsoprovidesauniquesummaryofhowdifferenttechnologiescouldbeexpectedtoimpactitsfuture.Wehopeitalsoprovidesinsightsforbothbusinessesandauditors

themselvesonhowtheymayadaptmosteffectivelyinthefaceofthissignificantchange.

Contents

Executivesummary6

1.Whatisdrivingtechnologicalchangeinaudit?7

2.Whichtechnologiesarechangingaudit?9

3.Whatdoesthismeanforauditorsaspeople?16

4.Conclusionandkeymessages19

References20

6

Executivesummary

Thelatestadvancesintechnologypromisesignificantbenefitsfortheauditprofession,witha

numberofkeydriverssignallingtheneedfortechnologicalchangeinaudit.Suchdriversincludetherapidincreaseinvolumeofdata,changesinbusinessmodels,theshifttowardsautomationandthedemandforaproactiveandforward-lookingapproachtoaudit.Thesedevelopments

requireauditorstobetechnologicallysoundtoenablethemtocontinueservicingbusinessesandtoexecutehighqualityaudits.

Theseconclusionsaresupportedby

paneldiscussionsheldinGreece,theCzechRepublic,andSlovakia,with

panellistsrepresentingauditpractice,auditregulatorsandthebusinessside,alongwithtechnologyexperts.The

reportisalsosupportedbyinterviewswithauditpractitionersandtechnologyexpertsintheauditprofession.

Inthisreport,ACCAandCAANZprovideanoverviewofthevarioustechnologiesthatcurrentlyaffectorarelikelytoaffecttheauditprofessioninthenearfuture.

Suchtechnologiesincludedistributed

ledgertechnology(DLT),dataanalytics,roboticprocessautomation(RPA),dronestechnology,artificialintelligence(AI)andmachinelearning(ML),naturallanguageprocessing(NLP),anddeeplearning(DL).Referenceisalsomadetosmartcontractsandcloudtechnologies.

Ourresearchfoundauditorsneedingtounderstandthevarioustechnologiesusedbybusinesses,andadapttothechangesintheirbusinessmodels.

Dataanalyticswasfoundtobethemostmatureofthetechnologiescurrentlyusedbymostfirms,whilemachinelearningisstillnotatthestagewhereitisembeddedineverydaypractice.ACCAandCAANZalsoexploredwhatthismeansfor

auditorsaspeople,withthehuman

relationshipbetweenbusinessand

auditoremphasised,becausethisremainscentraltoauditing.Insum,theauditors

ofthefutureshouldbetechnologically

soundwithexcellentprojectmanagementskillsandabletoadapttochange.

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1.

Whatisdrivingtechnological

changeinaudit?

INCREASEINVOLUMEOFDATA

Thevolumeoftransactionsanddatainbusinesseshasincreaseddramatically

since2016andisexpectedtokeep

increasinginthefuture.Ithasbeen

estimatedthatover90%oftheworld’s

datahasbeengeneratedsince2016,

andsignificantamountsofitarefinancialdata(Marr2018).

Thisrapidincreaseinthevolumeof

datarequiresauditorstobeequipped

withthelatestavailabletechnological

toolstoanalyseamuchhighervolume

ofdataintheirauditsthanhaspreviouslybeenthecase.

CHANGESINBUSINESSMODELS

Businessesacrossalmosteveryindustry

areinthefrontline,experiencingatfirsthandthedisruptivechangesthatarealsoaffectingtheirauditors.Disruptivechange

needstobedistinguishedfrom

innovationandtechnologyperse:thekeytodisruptionisthatitcreatesinnovationinbusinessmodels,newwaysofworkinginmarketsandnewsourcesofvalue.

Disruptioncanbeenabledbytechnologybutneednotinvolvetechnological

breakthroughs:itcaninvolvesimply

puttingexistingtechnologiestogetherinanewway.Forexample,afooddeliveryappsuchasJustEatorDeliveroorestson

someverybasicunderlyingtechnology

–kitchens,bicyclesandasmartphone

app–butputsthemtogetherinaway

thatradicallychangesthewayusersorderfood:aggregatingrestaurantsattheuserendandallowingnewplayerstoenter

themarketattheother,orserviceitindifferentways(‘darkkitchens’).

‘Auditorsmustbeabletoadapttothechangesinbusinessmodelsoftheirclients.’

JurajSekera,FinanceDirector,VertivSlovakia

Suchtechnologicalchangesinbusinessesandtheirbusinessmodelsrequirethe

attentionofauditorsofanysizeincludingsmallandmedium-sizedpractices(SMPs).Forexample,start-upbusinessesnow

tendtohavebusinessmodelsbasedonadvancedtechnologies.Complexauditchallengescouldthereforecomefromsmallerbusinessestoo.

‘Bothourauditorsandusarealreadyusingadvanceanalyticsandweexpecttoadoptblockchaintechnologyassoonasthesupplychainofthesteeltradeadoptsit.’

MichalisSamonas,CFO,SIDMAS.A.Greece

‘UnderstandinghowtechnologiessuchasBlockchain[and]MachineLearningworkisnecessarytoenableauditorstoassessandrespondtothecurrentandprospectiverisksoftheorganisations

thatplacetheirtrustinus.’

DimitrisSourbis,AssurancePartner,PwC

SHIFTTOWARDSAUTOMATION

Themostimmediateimpactof

technologyontheprofessionisinthe

automationoreveneliminationofmanualandroutinetasks.Thismovementis

acceleratedbecauseithasmultiple

drivers.Theshifttocloud-based

accountingsystemsandtheattendant

standardisationofprocesseshasmade

datamoreeasilyandmorewidely

available,easiertomovebetween

systems,easiertomanipulateand

analyse,andlesspronetocorruptionanderrors.Forexample,wheredatacannotmoveseamlesslybetweensystems,theuseofroboticprocessautomation(RPA)

canremovetheneedformanual

interventiontocoverthe‘lastmile’.

Despitethis,thereseemstobelittle

appetitefor‘human-free’audit–

automationcanreduceerrorsand

spotpatterns,butthatmerelyprovides

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Auditandtechnology|1.Whatisdrivingtechnologicalchangeinaudit?

70%

ofthegeneralpublic

across11countries

believethataudit

shouldevolvetopreventcorporatefailure

theopportunityforindividualsto

exercisethoughtandjudgement,and

tobringintoplayotherskillssuchas

communication,persuasionandempathy.

Auditorsmayfindtheyareasked

tolookintofeweranomalies–butthesewillbetheonesthatcount.Itseemsthattheroleoftheauditorasfilter,narratorandindependentchallengingvoice

remainssecure.

THEDEMANDFORAPROACTIVE

ANDFORWARD-LOOKINGAPPROACHINAUDIT

TheuseofadvancedtechnologiessuchasAIandML,blockchainanddata

analyticspromisesatransformationintheauditprofession,changingauditfromareactiveandbackward-lookingexercisetoaproactive,constantsourceof

forward-lookinginsightsthatcanbeused

allthetime,withtheauditorasthecustodianandinterpreterofthe

underlyingdatafoundation.

Eveninitstraditionalcontext,technologynowoffersanopportunitytoproduce

higher-qualityauditsthatbetterservefortheirexistingpurpose.

‘Inordertomeetsociety’sexpectationsoftodayandtoremainrelevantwithintheenvironmentweoperate,wehave

theresponsibilityoftransformingthe

waywedeliveronourobligationto

thepublic.Thistransformationincludesourresponsetotheadvancesof

technology-basedsolutions.’

DimitrisSourbis,AssurancePartner,PwC

ACCA’sreportClosingtheExpectation

GapinAuditfoundthat55%ofthe

generalpublicacross11countriesbelievethat,ifauditorsfollowedtherequirementsofexistingauditingstandards,theycouldpreventcorporatefailure(ACCA2019a).Furthermore,70%believethataudit

shouldevolvetopreventcorporatefailure.Althoughsomemayreasonablyarguethatsuchdemandsareunrealistic,technologycouldwellhelptosatisfythepublic

demand,atleastpartly,inthefuture.

AnexamplewouldbetheuseofMLinriskassessment,‘supervisedlearning

algorithmscanbeusedtohelpidentifyspecifictypesorcharacteristicsthat

warrantgreaterscrutinyandimprovetargetingareasoffocusfortheaudit.’(ACCA2019b).

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2.

are

Whichtechnologieschangingaudit?

Businessesacrossalmosteveryindustryareexperiencingatfirsthandthedisruptivechangesthatarealsoaffectingtheirauditors.Thischapterexploreswhichadvancedtechnologiesare

impactingtheauditprofession,referringtoboththetoolsavailabletoauditorsandthesystemsthatneedtobeaudited.

InApril2019,ACCAsurveyedmembers

andaffiliatesabouttheirunderstanding

oftermssuchasartificialintelligence(AI),machinelearning(ML),naturallanguageprocessing(NLP),dataanalyticsandroboticprocessautomation(RPA).Onaverageforanygiventerm,62%ofrespondentshadnotheardofit,orhadheardthetermbutdidnotknowwhatitwas,orhadonlya

basicunderstanding.Onaverage,only13%ofrespondentsclaimeda‘high’or‘expertlevel’ofunderstandingoftheseterms.

There’saneedforgreaterawarenessofwhatthesetechnologiesareandtheirimplicationfortheauditprofession.

ARTIFICIALINTELLIGENCE(AI)

AIisoftendescribedas‘a(chǎn)nevolving

technology’thatisequippingcomputersystemswithsomethingakintohuman

intelligence,butitisbetterseenasan

umbrellatermforagroupoftechnologiesthatcanbecombinedindifferentways,

whetherfordrivingyourcar,controllingyourcentralheating,ormanagingyourinvestmentportfolio.Itisalsothesubjectofalargeamountofhype,with‘human-likeintelligence’predictedtoappearin2029(orwhateverthecurrentdateisplustenyears)andeitherdrasticallyreducing

theworkforceordestroyingusall.

AccordingtoElonMusk:‘withartificialintelligencewe’resummoningthe

demon’(Finamore,E.andDuttaK2014).

Itcouldbearguedthatbecauseofa

lackofunderstandingofconceptssuchas‘intuition’and‘thought,’wedonotevenknowwhatitiswearetryingto

emulate.Isintelligencewhatis

measuredbyan‘IntelligenceQuotient’(IQ)?Or,indevelopingAI,shouldwebetryingtoemulateotherquotients,suchasan‘EmotionalQuotient’(EQ).

‘Itisclearthatsometaskswillnolongerbedonebytheauditors.Inthelongterm,itislikelythattheprofessionwillseea

shiftinitsfocuswithmoreemotional

intelligenceexpectedfromauditorsratherfocusingondatatesting.’

MichalStepan,AssuranceDirector,DeloitteCzechRepublic

The‘intelligence’inAIoftenconstitutesacombinationofprocessingpowerandaccesstodata:forinstance,acomputerwillplayagamesuchaschessby

analysingallthepossibleoutcomesofamove,usingdatasetsfrompastgamesandselectingthewinningoption.

ButthatfactalonemakesAIhighly

usefultopeople:itenablestheanalysisofentirepopulationsofdatatoidentifypatternsorexceptions.Auditorsare

freedfrommundanetasksandcanfocustheirtimeondeployingtheirskills,

trainingandjudgement:although

technologyismakingprogressinareas

suchasspeechprocessingandsentimentanalysis,professionaljudgementismuchhardertoapplytechnologyto.

ROBOTICPROCESSAUTOMATION

(RPA)

RPAisoftenmistakenlythoughtofasaformofAIbutthe‘robots’are

softwareroutinesthataremorelikeverysophisticatedExcelspreadsheetmacrosthangenuineAI.

AshighlightedinACCA’sjointreport

withCAANZandKPMGEmbracing

roboticautomationduringtheevolutionoffinance,‘RPAissoftwarethatcan

beeasilyprogrammedorinstructedbyenduserstoperformhigh-volume,repeatable,rules-basedtasksin

today’sworldwheremultipleloosely

integratedsystemsarecommonplace.’(ACCAetal.2018).

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RPAiscommonlyusedwhentheoutputofonefinancialprocessneedstobeinputintoanother,orwheremultiplesourcesofinformationneedtobeconsulted.Asa

result,itissometimesreferredtoas

‘swivelchairautomation’,conjuringup

theimageofanemployeeswivellingtheirchairaroundastheyconsultmultiple

systemsandre-keyandcheckinformation.

Auditandtechnology|2.Whichtechnologiesarechangingaudit?

Thenextstepfor

auditorsandfinanceistoapplyAIandMLalgorithmstoimprovethequalityofanalysisandforecasting,andincreasetherateof

frauddetection.

Suchworkisrepetitive,mundane,time

consumingand,whendoneby

individuals,pronetoerror.Itisalso

difficulttoscaletocopewithvariationsinworkload.Aclassicexamplewouldbe

processingtimesheetinformationfromseasonallyemployedtemporarystaff.

Onesolutionistodeployorleasea

‘robot’,asoftwareroutinethatpreciselymimicstheactionsofthechair-swivellingpersonshiftingbetweensystems.Lookingbacktothetimesheetexample,therobotwouldtaketheinformationgatheredbyopticalcharacterrecognition(OCR)fromthepaperrecordsandfeeditintothe

payrollsystem.Becauseitmimicsa

processratherthananalysingdata,RPA

itselfisnotAI,whichcouldbeusedlatertolookfortheanomaliesthatpreviouslyahumanoperatormighthavehadtospot.

RPAoffersmanybenefits:therobotsworknon-stopandarefaster,moreaccurate

andscalable.Nonetheless,therearealsoquestionsaboutaccountabilityand

ownershipoftheRPAprocessand

securityofthedatathatpassesthroughit.

Thereisalsothequestionofwhether

RPAsimplyperpetuatesinadequate

processesthatshouldhavebeen

overhauled.Wecandistinguishbetween‘goodRPA’,whichclosesgapsand

contributestostraight-throughdata

processingand‘badRPA’,whichsimplydisguisestheflawsinobsolescentor

badlyimplementedsystems.Inshort,fixtheprocessfirstbeforeapplyingRPA.

DATAANALYTICS

Analyticaltoolshavelongbeenappliedtothedataderivedfromaccountingandoperationalsystems.

Somefirmsarealreadyusingdataanalyticsaspartoftheirtransactionstesting,

graduallymovingawayfromtraditional

samplingtechniques.Dataanalyticsallowauditorstouse100%ofapopulation’s

transactionswhenperformingtheirtests.

‘UsingD&Awemaketheanalysisofthe

pastmoreinsightful.Ratherthan

samplingtransactionsdatatotesta

snapshotofactivities,wecannowanalyzealltransactionsprocessed,allowingustoidentifyanomaliesanddrilldownontheitemsthatshowthegreatestpotentialofbeinghighrisk.Oursystemsautomatethisprocess,increasingitsabilitytoproducehighqualityauditevidence.’(KPMG2015).

‘Iexpectthatmyauditorswillno

longertestasampleoftransactions,for

example100items,andconsiderthisto

besufficientevidencetoformaconclusionfortheentirepopulation,wheninfactwehavetensofthousandsoftransactions

cominginandoutonadailybasis.’

JurajStriezenec,CFO,K

However,theUKFinancialReportingCouncil(FRC)hasfoundthat‘theuseofdataanalyticsintheauditisnotasprevalentasthemarketmightexpect’(FRC2017)anditisnotyetused

consistentlyacrosstheentireledger.

Evenwhereitisused–suchasinjournalentrytesting,auditorswillstillneed

toconsidertheissueofcompleteness,aswellastheincreasingamountof

corporatereportingthatdoesnot

derivefromtransactionsintheledger.

‘Beingabletotest100%ofapopulationdoesnotimplythattheauditorisabletoprovidesomethingmorethanreasonable

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Auditandtechnology|2.Whichtechnologiesarechangingaudit?

‘Weareinvestingin

varioustechnologiestosupport“digitalaudit”,withdataanalytics

beingatthemomentthemostprominent.’

LeonidasHatzikonstantis,

PartnerEYAdvisoryServices,Greece

assuranceopinionorthatthemeaningof“reasonableassurance”changes.’(IAASB2016)

ThenextstepforauditorsandfinanceistoapplyAIandMLalgorithmstoimprovethequalityofanalysisandforecasting,

andincreasetherateoffrauddetection.

Thebusiness‘datawarehouse’is

increasinglybeingsupplementedby

informationdrawnfromavarietyofpublicand/orproprietarysources,oftenusing

cloud-basedapplicationscombinedwithdesktopanalyticaltools.Augmenting

thesetoolswithDLandNLPincreasestherangeofdatathatcanbehandled,fromwrittentextspeechorevenimages.

Theabilitytoanalysedataacrossand

outsidecorporatedatasilospromisestoenhancetheabilityoforganisationsto

spotopportunities,headoffthreats,

makebetterdecisionsandenablethis

processtobe‘democratised’throughouttheorganisation.

Auditorscanuse‘dataminingsoftware’todrilldownandidentifyanomalies–

possiblyaidedbyAI–focusingresourcesonidentifyingrisksinadditionto

monitoring‘businessasusual’activities.

MACHINELEARNING(ML)

Amajorchallengetotheauditprofessionhasbeentheextremeproliferationof

data,accompaniedbyalessextremebutnonethelessrapidlyexpandingvolumeofregulation.

AccordingtoACCA’sreportMachineLearning:MoreSciencethanFiction:

‘Therapidgrowthinthevolumeof

financialtransactions,ifnotproperly

managed,couldposeathreattotheworkofaccountants.Forauditors,thismay

relatetothesampletheyneedanditsabilitytoberepresentativeofthe

population,enablingthemtoformconclusionsthatcanbegeneralisedbeyondthesample’(ACCA2019b).

‘Infact,technologylikemachinelearningcouldgobeyondthatwiththepossibilityforreviewingentirepopulationstoassisttheauditortotestforitemsthatare

outsidethenorm’(ACCA2019b).

MLusesstatisticalanalysestogeneratepredictionsormakedecisionsfromtheanalysisofalargehistoricaldataset.Aclassicexamplewouldbecreditscoring

decisionsforloans.Theaccounting

softwarecompanyXerohasimplementedMLtomakecodingdecisionsforinvoices.MLcanachievesurprisinglevelsof

accuracyquitequickly:inthecaseofXero’ssoftware,thesystemachieves80%accuracyafterlearningfromjustfourinvoices.

ML‘predictions’canbebothbackward

andforward-looking.Ithasclear

applicationsinriskmanagementandthedetectionoffraudandinaccuracyby

comparinghistoricaldatasetswithcurrentdata,whichcanhelpwithriskassessment.Oritcanlookforward,predicting,for

example,thelikelyfuturevalueofanasset.

Inpractice,theusefulnessofMLis

cruciallydependentonthedatait‘learns’from.Thismeansthepossibilityofbiasiseverpresent.Exampleshavecometo

lightwhereMLhasintroducedbiasintoareassuchascredit-scoringandCV

assessment.Themachinecorrectlyseesthatapreviouslyexcludedgrouphadnotcompletedmanysuccessfulloan

transactionsorrisenveryhighin

managementandwronglyconcludedthatthedefiningcharacteristicsofthose

groups,suchasgender,werepredictorsofpoorfutureperformance.

AnexampleusingMLinauditcanbe

foundinPwC’sreportConfidenceinthefuture:Humanandmachinecollaborationintheauditreport.Asperthisexample

‘companyAwaswayoutoflinewiththepeergroupbenchmarksonaparticularpoint.Thisdataisthensharedwiththeauditteam,whocandecidewhetherthatvarianceisreallyananomalyandifso,

whatcausedit.

Theteam’sdecisionabouttheanomalyanditscauseisthenfedbacktothe

machine,whichis‘taught’howto

respondtosimilarrelationshipsinfuture.Andthemorethisexerciseiscarriedout,thebetterthemachinewillgetat

spottingrealanomalies—meaningwe’llbebetterabletoidentifyunusual

patternsandanomaliesinhugeamountsofdatainaninstant.’(PwC2017).

Theself-instructingnatureofMLmeansthatdecision-makingcanoftenbea‘blackbox’,withnooneabletosayprecisely

howdecisionshavebeenarrivedat.

Thereisalsothedangerthatduringthelearningstage–whenMLisshadowinghumanauditors–itwillpickupany

humanerrorsandrepeatthemeternally.

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Auditandtechnology|2.Whichtechnologiesarechangingaudit?

IntheeraofBig

Data,thestructured

informationaccessibletoauditorsisonly

afragmentoran

abstractionofthemuchwideruniverseofdata.

MLthereforeneedstobevalidatedinsomeway:itisariskaswellasatool.

Thisraisesthepossibilitythatthe

challengingandtestingofinternal

algorithmsmaybecomepartofthe

externalauditor’srole,withamuchwiderremitthanassessingaccuracy:asthe

HarvardBusinessReviewcomments:

‘theauditor’staskshouldbethemore

routineoneofensuringthatAIsystemsconformtotheconventionsdeliberatedandestablishedatthesocietaland

governmentallevel’(Guszczaetal.2018).

NATURALLANGUAGEPROCESSING(NLP)

NLPreferstotheabilityofthecomputertorecogniseandunderstandhumanspeech